Why Do Green Trees Turn White in Infrared Photographs?

The trees are green, the sky is blue — so why does an infrared camera disagree?

Imagine photographing a familiar landscape: a leafy tree beside a river, grass along the bank and a few clouds drifting overhead.

In an ordinary photograph, everything looks reassuringly familiar.

Now photograph the same scene in near-infrared.

The foliage may appear almost white. The grass becomes pale and luminous. The blue sky turns dramatically dark, while clouds stand out against it.

It can look like a summer landscape transformed into a winter dream.

Yet there is no frost on the leaves. The camera is recording light that our eyes cannot see.

For me, this is one of photography’s most interesting possibilities: a familiar place can become a way of exploring unfamiliar science.

First, What Is Near-Infrared Photography?

Human vision covers only a small part of the electromagnetic spectrum, approximately from violet at 400 nanometres to deep red at 700 nanometres.

Beyond red lies infrared.

For this kind of landscape photography, we are interested in near-infrared, commonly recorded at wavelengths between roughly 700 and 1,000 nanometres, depending on the equipment.

The important distinction is that these photographs primarily record reflected light, usually from the Sun. They are different from thermal images, which detect longer infrared wavelengths emitted by objects at everyday temperatures.

A white tree in a near-infrared photograph is therefore not necessarily warmer than its surroundings. It is returning a strong near-infrared signal to the camera.

The Wood Effect: A Summer Tree That Looks Snow-Covered

The bright appearance of foliage in infrared photographs is known as the Wood effect, named after the infrared photography pioneer Robert W. Wood.

Despite the name, it refers to the behaviour of foliage rather than timber.

To understand it, we need to look inside a leaf.

A leaf is much more than a thin green surface. It contains layers of cells, pigments and internal air spaces. Together, these determine which wavelengths are absorbed, transmitted or scattered back out.

In visible light, chlorophyll absorbs strongly in the blue and red regions. Relatively more green light escapes, which is why most healthy leaves look green.

Beyond the visible red region, the balance changes. Pigment absorption becomes much weaker, while the leaf’s internal structure scatters near-infrared radiation strongly.

Some of that radiation passes through the leaf; some emerges back towards the camera. The strong reflection arises from repeated scattering at boundaries between materials with different optical properties inside the tissue.

The leaf’s green appearance tells us about its pigments. Its near-infrared brightness tells us much more about its structure.

That is a lovely connection between photography, physics and plant biology.

Why Does That Become White in the Photograph?

Near-infrared radiation has no colour that our eyes can directly perceive.

The camera and editing software must turn the recorded signal into something visible.

In a black-and-white infrared image, a strong signal can be represented by a light grey or white tone. A weaker signal becomes darker.

That is why strongly reflecting foliage can appear white.

However, white leaves are not guaranteed straight out of the camera. An infrared file may initially look reddish, pinkish or otherwise unfamiliar. White balance, filter choice, exposure and processing all influence the final result.

The bright foliage has a physical basis; its exact appearance on screen also involves photographic choices.

Infrared photography combines measurement with interpretation.

Why Does the Blue Sky Often Turn Dark?

The blue of a clear sky comes largely from Rayleigh scattering.

Air molecules scatter shorter wavelengths much more strongly than longer ones. Blue light is scattered strongly into our line of sight; near-infrared is scattered much less.

Consequently, a clear patch of sky can deliver a relatively weak near-infrared signal and appear dark in the photograph. Clouds scatter light differently because their droplets are much larger than air molecules, so they can remain bright against that darker background.

This creates one of infrared photography’s most striking combinations:

Pale foliage, dark sky and bright clouds.

The sky will not always become black. Haze, cloud cover, viewing direction, filter choice and editing all affect the result.

Grass, Leaves, Wood and Water: Expect Different Responses

A useful starting point is to stop assuming that an object’s visible colour predicts its infrared brightness.

Subject	Common near-infrared appearance	What to notice
Leafy trees	Bright, sometimes almost white	Species, leaf structure, lighting and canopy density affect the result.
Grass	Often pale and bright	Living grass may separate strongly from paths, soil and shadows.
Bark and dead wood	Often darker than foliage, with varied tones	Texture, moisture and surface condition matter; wood does not automatically show the foliage effect.
Open water	Frequently dark	Absorption and reflections both influence its appearance.
Clouds	Often bright against clear sky	Cloud type and lighting determine how dramatic the contrast becomes.

Water is particularly interesting. It often looks dark because relatively little near-infrared returns from within it, but its surface can still reflect bright clouds, foliage or sunlight.

A river can therefore contain both dark stretches and bright reflections.

For a Thames landscape, I would look for a composition combining leafy banks, open water and a distinctive tree. That gives several different materials to compare within one frame.

Sunlight Matters — but So Does Wind

Near-infrared photography depends on illumination.

Direct sunshine often makes a useful starting point because it supplies plenty of near-infrared radiation and can create strong separation between sunlit foliage, shadows and sky.

A bright day with some cloud can be especially rewarding.

This offers an interesting change of approach for landscape photographers. The midday sunshine that can feel harsh in conventional photography may produce striking infrared results.

Overcast conditions can still work, although the effect may be gentler and exposures may need to be longer.

Wind creates a different problem.

If your setup needs a long exposure, moving leaves may blur even when the trunk remains sharp. That can be an attractive creative effect, but it makes a scientific comparison harder.

For a first attempt, choose bright conditions and a relatively still tree.

In autumn, look for a tree still carrying plenty of leaves, or try an evergreen. Repeating the exercise as the seasons change would make an interesting longer project.

What Equipment Do You Need?

Most ordinary digital cameras contain an internal filter that suppresses infrared, helping them produce reliable visible colours.

There are two common ways to record near-infrared:

• Use an infrared-pass filter on a suitable unmodified camera. This blocks most visible light, but the camera’s internal infrared suppression may mean long exposures or very limited results.

• Use a camera converted for infrared, or a full-spectrum camera with an appropriate filter. These arrangements can record near-infrared more efficiently.

Compatibility matters: an external filter does not make every camera equally capable of infrared photography.

A filter around 720 nanometres is a common starting point for infrared work and can support both monochrome and some false-colour processing. Longer-wavelength options, such as 850 nanometres, tend towards a more strongly monochrome appearance, with different exposure requirements.

For this exercise, the aim is to use a setup known to record near-infrared reliably.

Practical Demonstration: One Tree, Three Interpretations

Your task: photograph the same tree in visible light and near-infrared, then compare both with an ordinary black-and-white conversion.

What you need

• A normal camera or phone.

• A suitable near-infrared camera and filter arrangement.

• A tripod, particularly if infrared exposures are long.

• A leafy tree, ideally with sky and another material in the frame.

• Editing software capable of producing a monochrome image.

1. Choose a useful scene

Include leaves, trunk, sky and grass. If possible, add water, a wall or a path.

The different surfaces make the comparison more revealing.

2. Take the visible-light photograph

Record the scene normally.

Note the conditions: sunshine or cloud, time of day and whether the leaves are moving.

3. Take the near-infrared photograph

Keep the viewpoint and framing as similar as possible, and photograph it soon afterwards so the lighting remains comparable.

Use settings appropriate to the infrared setup rather than copying the visible-light exposure. Check focus and the histogram, protecting detail in the brightest foliage.

4. Make a normal black-and-white version

Convert the visible-light photograph to monochrome.

Now you have three images:

1. Visible colour.

2. Visible light converted to black and white.

3. Near-infrared rendered in black and white.

That third comparison is especially useful. Removing colour from a normal photograph does not reveal infrared information that the camera did not record.

5. Compare what changed

Ask:

• Did the leaves become brighter relative to the trunk?

• Did the grass separate more clearly from the path?

• Did the sky darken?

• Did clouds become more prominent?

• Which objects changed least?

Keep the processing reasonably restrained at first. Extreme contrast adjustments can make the images dramatic while obscuring what you are trying to compare.

Turn the Experiment into a Creative Landscape

Once you understand the differences, use them deliberately.

A pale canopy can become the main subject against a dark sky. A shaded trunk can provide structure within bright foliage. A path can lead the viewer through an otherwise luminous scene.

Look for:

• An isolated tree with a recognisable outline.

• A winding path through leafy woodland.

• Bright foliage beside dark water.

• Architecture framed by vegetation.

• Clouds that balance the shape of a tree.

When editing, preserve texture in the leaves. If every bright area becomes pure white, much of the interest disappears.

Deep blacks can be effective, but the strongest image does not always need the most extreme contrast.

The question I would ask is: what does infrared reveal that makes this particular composition more interesting?

Can an Infrared Photograph Tell You Whether a Plant Is Healthy?

Near-infrared observations are valuable in vegetation monitoring because changes in plant tissue and canopy structure can affect the recorded signal. Scientists compare visible and near-infrared measurements to investigate vegetation.

However, a bright tree in a processed photograph is not, by itself, a reliable plant-health diagnosis.

Species, illumination, leaf angle, exposure and editing all affect brightness. Scientific assessment needs controlled measurements and appropriate interpretation.

For teaching, that limitation is useful. It opens a discussion about the difference between a striking observation and sufficient evidence for a conclusion.

A Familiar Tree Can Teach Us to See Differently

What appeals to me about this demonstration is that the subject can be completely ordinary.

There is no need to travel to an exotic location. A tree in the garden or beside a familiar riverside path can connect photography with pigments, living tissue, scattering and the electromagnetic spectrum.

The tree has not changed between photographs.

The wavelengths we record have changed, revealing a different set of properties.

This week’s challenge: choose one tree and make the three-image comparison.

Look beyond the white leaves. Study the trunk, the shadows, the sky and the ground beneath it.

Our eyes give us a wonderfully useful view of the world. Infrared photography reminds us how much more there is to discover.

 

Could Every Student Have Their Own AI Revision Course?

Why should two students be given exactly the same revision sheet when their weaknesses are completely different?

Two students sit the same mathematics test. Both score 50%.

On paper, they appear to need roughly the same help. Give them another revision worksheet, explain the difficult questions and encourage them to practise.

But look more closely.

One student understands the mathematics but struggles to translate written questions into equations. The other can set up the equations perfectly well but makes mistakes when rearranging them.

Their marks are identical. Their learning needs are very different.

This is where AI could become particularly useful: helping a teacher build a revision course that changes as the student learns.

Imagine a course that keeps track of topics covered, mistakes made, hints needed and confidence reported. Each new task would be chosen using that evidence.

That is a much more interesting possibility than simply asking AI to produce another twenty questions.

From a Revision Folder to a Changing Learning Plan

A conventional revision course usually has a fixed structure:

Read the notes. Watch the explanation. Answer the questions. Check the answers. Move to the next topic.

There is nothing inherently wrong with that. A clear sequence can be extremely helpful.

However, students rarely develop at exactly the pace assumed by the course. Some need to revisit earlier knowledge. Others are ready for harder applications. Some can complete a question immediately after an explanation but struggle when it returns a week later.

An individual AI-assisted course could respond to these differences.

It might suggest:

• a short diagnostic task when the cause of a mistake is unclear;
• a worked example when a method needs explaining;
• an independent question to check whether the explanation helped;
• a harder application when the student is ready;
• a later review to check whether the learning has lasted.

The useful feature is the decision about what comes next.

Generating resources is only one part of the job.

The Student Profile: More Useful Than a Single Percentage

For this approach to work, the course needs a useful record of learning.

That record need not be an enormous database. A carefully maintained table could provide a starting point.

Here is an illustrative profile for a fictional GCSE mathematics student:

Area	Evidence from recent work	Confidence	Possible next step
Solving linear equations	Solves simple equations independently; errors with brackets	Fairly confident	Check expansion before harder equations
Simultaneous equations	Solves a prepared pair; struggles to form equations from a situation	Low	Practise translating statements into equations
Fractions	Adds simple fractions; inconsistent with mixed numbers	Low	Short diagnostic task
Graphs	Plots accurately; confuses gradient with intercept	High	Explain and compare the two
Circle calculations	Uses the formula correctly once radius is identified	Moderate	Practise identifying radius and diameter

The confidence column is particularly revealing.

A student who is confidently wrong needs different feedback from one who is correct but convinced they cannot do the subject.

However, confidence is self-reported information, not proof of understanding. It should sit alongside actual work.

I would also want to record whether an answer was produced independently, after a small hint, or while following a complete example.

Those are three different levels of support. A course should recognise the difference.

A Practical Example: What Is Going Wrong With Simultaneous Equations?

Suppose a student struggles with this question:

Two adult tickets and one child ticket cost £23. One adult ticket and two child tickets cost £19. Find the price of each ticket.

There are several possible obstacles.

The student might not understand what the letters represent. They might struggle to form the equations. They might know the method but make a subtraction error. They might find the wording overwhelming.

Giving them ten more questions of the same type could repeat the difficulty without explaining it.

A better next step is to investigate.

Step 1: Separate the Algebra From the Interpretation

Present the equations directly:

2a + c = 23
a + 2c = 19

Can the student solve them?

If they can, there is evidence that the main difficulty may lie in translating the situation into mathematics. That is a working hypothesis, rather than a final diagnosis.

If they cannot, check the underlying algebra.

Can they solve:

3x = 12?

Can they rearrange:

x + y = 9

to make y the subject?

Can they subtract one equation from another without changing signs incorrectly?

The next task should help distinguish between these possibilities.

Step 2: Give Practice That Addresses the Evidence

If interpretation appears to be the obstacle, ask:

An adult ticket costs a pounds and a child ticket costs c pounds. Write an expression for the cost of two adult tickets.

Then:

Write an expression for two adult tickets and one child ticket.

Then:

Their total cost is £23. Turn your expression into an equation.

The student is practising the part that caused the difficulty.

If algebra is the obstacle, work with simple prepared equations first, then return to the ticket problem.

Step 3: Return to the Complete Question

The original problem gives:

Adult ticket = £9
Child ticket = £5

The student should check both conditions:

2 × £9 + £5 = £23
£9 + 2 × £5 = £19

Then try a fresh question independently.

Successfully following an explanation is encouraging. Solving a new problem provides stronger evidence that the method is understood.

Step 4: Check Again Later

Revisit the skill in a later session, with different wording and numbers.

If the student can still use it independently, the profile can record stronger evidence of progress.

The course should respond to what the student can do today, while checking what they can still do later.

Personalisation Should Sometimes Make Work Harder

It is easy to imagine personalised learning as a system that keeps making questions easier.

That would be a serious limitation.

A useful course should also recognise when a student needs greater challenge.

For simultaneous equations, that might mean progressing towards:

• equations requiring multiplication before elimination;
• negative or fractional solutions;
• unfamiliar contexts;
• questions where the student must choose the method;
• explaining why someone else’s solution is incorrect.

The aim is to make success increasingly independent.

A student who has mastered the basics should not spend an entire revision session repeating them simply because those questions are easy to generate.

Equally, a student who struggles should not be permanently confined to elementary work. The course needs a route back towards the demands of the examination.

The Same Approach Could Help in Science

Consider a biology student who writes:

Antibiotics kill viruses.

A useful response should do more than replace that sentence with the correct one.

The next task could ask the student to distinguish between a bacterial disease and a viral disease. A further question could check whether they understand why antibiotics are unsuitable for treating a viral infection.

Later, the course could revisit the idea within a question about antibiotic resistance.

In physics, a wrong calculation might lead to separate checks on:

• selecting the equation;
• rearranging it;
• converting units;
• substituting values;
• interpreting the answer.

In chemistry, an incorrect concentration calculation might reveal a weakness in unit conversion rather than chemical understanding.

The same principle applies across subjects:

A wrong answer is the beginning of an investigation.

It does not automatically reveal its own cause.

Confidence, Independence and Knowing When You Need Help

One of the most valuable questions a revision course could ask is:

Before you check, how confident are you that your answer is correct?

Followed by:

What makes you think that?

Over time, students could compare their judgement with their performance.

Some might discover that they know more than they think. Others might realise that recognising a familiar question is different from being able to solve it.

This connects with metacognition: learning to plan, monitor and evaluate your own learning. The Education Endowment Foundation’s guidance emphasises explicitly teaching and modelling these processes within subject teaching. That supports the educational principle behind this approach; it does not establish that any particular AI course will deliver it successfully. EEF

An AI-assisted course could prompt that reflection.

A teacher still needs to help students make sense of it.

My Perspective as a Tutor: The Record Must Leave Room for the Person

After around forty years of teaching, I am very aware that a student’s performance cannot always be explained by their subject knowledge.

A student may understand something during a conversation but struggle to express it on paper. They may rush, misread a word, lose concentration or arrive tired.

A record saying “three incorrect answers” cannot explain all of that.

In my own teaching, I would want AI to help organise the evidence and prepare useful follow-up work. I would still want to talk to the student, look at their working and ask why they chose a particular approach.

Sometimes a question needs simplifying.

Sometimes it needs drawing.

Sometimes we need to put the worksheet down and demonstrate the idea.

That matters especially in science. A student may answer questions about an experiment yet still need practice setting up apparatus, making measurements or explaining an unexpected result.

An individual revision course should be able to recommend practical work, discussion and handwritten practice alongside screen-based activities.

The Course Must Remain Anchored to the Examination

Personalisation needs a destination.

For examination students, the course must take account of the qualification, specification, tier where relevant, and the skills assessed.

A course could become very good at practising a narrow weakness while overlooking an entire section of the syllabus.

I would therefore want two views of progress:

The close view: What does this student need next?

The whole-course view: What remains to be covered and revisited?

Students also need mixed questions where the topic and method are not announced in advance.

A worksheet headed “Simultaneous Equations” provides a substantial clue. An examination may expect the student to recognise the need for simultaneous equations themselves.

Eventually, practice must include choosing the approach as well as carrying it out.

AI Can Produce Convincing Mistakes

A personalised course is only useful if its questions, explanations and feedback are dependable.

AI can produce an incorrect answer, an ambiguous question or an explanation that sounds persuasive but does not address the student’s error.

It may also infer too much from too little evidence.

One incorrect answer should not become a permanent label in a student profile.

For that reason, I would want teacher checks, verified solutions and a clear way to challenge the feedback. Uncertain conclusions should remain marked as uncertain.

The Department for Education’s guidance similarly stresses that generative AI cannot replace expert subject knowledge and professional judgement. GOV.UK

There is another practical limitation: a normal chatbot conversation should not be assumed to maintain a complete, accurate learning record indefinitely.

A continuing course needs a deliberately maintained record that can be reviewed and corrected.

A Learning Profile Should Contain Only What Is Needed

The educational value lies in information such as:

Confuses radius and diameter; correctly identifies both after a diagram prompt.

It rarely requires a student’s full name, home address, family circumstances or detailed personal history.

A sensible starting point is a minimal learning record, using a student code where appropriate, in a setting approved for the intended use.

Before introducing a tool, check its suitability for the student’s age and how it handles the information entered.

The profile should also be written respectfully.

“Needs further practice interpreting ratio questions” is useful.

“Bad at maths” is a judgement that offers no next step.

What Could a Twenty-Minute Session Look Like?

Here is one possible structure:

Time	Activity	Purpose
3 minutes	Recall questions from earlier work	Check what remains accessible
4 minutes	Short diagnostic task	Investigate a current difficulty
6 minutes	Explanation and targeted practice	Address the identified gap
5 minutes	Fresh independent application	Check whether support can be withdrawn
2 minutes	Reflection and record update	Plan the next session

The timings would change with the learner.

A student who needs more thinking time should receive it. Another may be ready to move quickly to a demanding application.

The record might end with:

Formed two equations independently. Needed one hint when subtracting negative terms. Next session: brief sign check, followed by a fresh word problem.

That is much more useful than simply recording “completed worksheet”.

A Small Starting Point Is Enough

Teachers and tutors do not need to begin with an elaborate automated platform.

A manageable trial could involve one topic, a small learning record and a teacher-reviewed set of follow-up tasks.

A useful instruction to AI might be:

Using the anonymised learning record below, propose a short revision session on simultaneous equations. Start with two diagnostic questions that help distinguish difficulty forming equations from difficulty solving them. Suggest different next steps depending on the answers. Include one independent application and a later review question. Provide checked solutions separately. Mark uncertain interpretations as hypotheses for the teacher to review.

This would produce a proposal for the teacher to assess.

The important test is whether the resulting tasks help the student learn more effectively—and whether the time spent maintaining the system is worthwhile.

Could Every Student Have Their Own Course?

Potentially, yes.

AI could make it more practical to prepare different explanations, tasks and review sequences for different learners.

But giving every student access to a chatbot would not, by itself, create an individual revision course. Access to suitable devices, clear guidance, dependable resources and appropriate support would still matter.

The strongest version would combine a carefully maintained learning record with teacher judgement and regular evidence of independent progress.

Two students might be working towards the same examination while following different routes through their revision.

One might need help understanding the question.

Another might need help selecting the method.

A third might be ready to explain why the method works.

They all deserve a suitable next step.

The most exciting possibility is that AI could help us act more consistently on something good teachers already know: the next question matters, and it should be chosen with the learner in mind.

 

Can AI Compose Music — And What Is the Musician Still For?

If AI can generate thousands of pieces of music, knowing which one is actually good becomes more important, not less.

Ask an AI system to create an uplifting instrumental, a melancholy piano piece or a dramatic soundtrack, and it may produce something remarkably convincing.

There may be a recognisable melody, a chord progression, percussion, a polished arrangement and even a singer. Services such as Suno offer both songs with vocals and instrumental music generated from written descriptions.

That raises an uncomfortable question for anyone who has spent years learning an instrument:

If a computer can produce all that, what is the musician still for?

My answer begins with another question:

What is this particular piece of music supposed to achieve?

A track can sound impressive and still be completely wrong for the job. It can overwhelm a spoken explanation, give a quiet scene the wrong emotional meaning or arrive at its dramatic climax several seconds after the important moment has passed.

AI can expand the supply of musical possibilities. Choosing, developing and using those possibilities requires judgement.

First, “AI Music” Covers Several Different Activities

There is a substantial difference between asking a chatbot for chord suggestions and asking a music generator to produce a finished recording.

There is also a difference between creating new material and using AI to separate an existing recording into component parts.

A useful distinction is:

• An ideas assistant: suggests themes, chords, lyrics, structures or approaches.

• An audio generator: produces music or sounds that you can hear.

• An analysis or production tool: helps inspect, separate or process recorded material.

These capabilities may overlap, but they are not interchangeable.

A written suggestion for a string arrangement is not a playable orchestral score. A stereo recording is not automatically a collection of editable instrument parts. A confident explanation of a chord progression is not proof that the analysis is correct.

The practical question is therefore: what kind of help do I need, and what will I actually be able to edit afterwards?

Ten Ways AI Can Help a Musician

1. Brainstorming themes: getting beyond the blank page

Sometimes the hardest part is deciding where to begin.

Suppose I am planning a short film about sailing on the Thames. “Write some sailing music” leaves a great deal unexplained.

A more useful starting point might be:

Suggest three musical approaches for a 45-second film showing an early morning river, a boat being rigged and the first moments under sail. Describe the mood, tempo, instrumentation and how the music could develop.

That could produce three directions:

• sparse piano and sustained textures for the quiet river;

• light percussion and a repeating figure for the preparation;

• a broader melody as the boat begins moving.

I might reject two and borrow one small idea from the third. That is still useful progress.

The value may lie in finding a direction, rather than accepting a finished answer.

2. Generating chord ideas: hearing different possibilities

Take a simple four-note phrase:

C – D – E – G

For a first experiment, give each note one beat.

An AI assistant could suggest several harmonisations. I could then play the phrase over C major, A minor or a more unsettled combination of chords and listen to the difference.

The important work happens at the keyboard.

Does the harmony support the phrase? Does it make the music too sentimental? Does a change of bass note create the movement I wanted?

On the Pergamon, this becomes an especially interesting experiment because I can change both the harmony and the registration.

However, I would change one thing at a time initially. Otherwise, I might mistake an attractive sound for a better musical idea.

3. Suggesting arrangements: deciding what happens next

A melody and some chords do not automatically provide an effective arrangement.

AI can suggest ways to introduce, develop and conclude material:

Outline a 30-second instrumental arrangement. Introduce a short motif, repeat it with a change of texture, build gently and finish with a clear ending. Leave space for narration.

That gives me something to test.

Perhaps the suggested percussion arrives too early. Perhaps the arrangement needs fewer layers. Perhaps the opening is already strong enough without an introduction.

An arrangement becomes useful when its development serves the purpose of the piece.

4. Analysing harmony: supporting learning

AI can also act as a discussion partner.

Provide a chord sequence and ask:

Explain the function of each chord in C major. Identify any ambiguity and suggest one alternative ending.

This can help a learner explore why a progression feels settled, unfinished, tense or surprising.

However, the explanation needs checking against the notes and the sound. A general chatbot may produce plausible musical terminology while overlooking an inversion or misunderstanding the key. An audio tool may also misidentify notes in a dense recording.

For teaching, I would ask a student to play the progression, explain it in their own words and compare alternatives.

That turns an answer into an investigation.

5. Creating lyrics: finding something worth singing

AI can suggest subjects, rhymes, verse structures and alternative lines.

That is helpful when the idea exists but the wording refuses to cooperate.

Yet lyrics must work in the mouth as well as on the page. A line may read beautifully but put the wrong syllable on the strongest beat. Another may rhyme neatly while saying very little.

A useful request would be:

Suggest four versions of this line, keeping the meaning and an eight-syllable pattern. Use everyday language and avoid clichés.

Then sing them.

Which words feel natural? Where does the singer breathe? Does the line sound like something this person would actually say?

Specific details often provide the individuality that a generic lyric lacks.

6. Producing reference tracks: making a conversation audible

Words such as “warm”, “cinematic” and “uplifting” can mean different things to different people.

A temporary reference track can help a client explain their preferences:

“The pace is right, but the brass is too grand.”

“I like the texture, but the melody draws attention away from the speaker.”

“That sounds like an adventure film. We are advertising a peaceful afternoon on the river.”

Those responses are useful.

A reference gives everyone something concrete to discuss. It should be clearly identified as a reference while the final musical direction is being agreed.

7. Separating stems: exploring an existing recording

A stem is an audio component or group, such as vocals, drums or bass.

AI separation tools can estimate these components from a mixed recording. Steinberg’s SpectraLayers, for example, provides an Unmix Song process for separating instrumental material into stems.

This can help with listening and study. Reducing the vocal may make an accompaniment easier to hear; isolating the bass may reveal how it supports the harmony.

However, separation does not recover the original studio tracks perfectly. Listen for watery sounds, softened attacks or fragments of other instruments.

It is also a production process, rather than composition in itself.

8. Generating sounds: finding an unfamiliar texture

Sometimes I need a sound rather than a tune.

Perhaps a soft metallic pulse, a slowly changing atmosphere or a brief transition between scenes.

Text-to-audio systems can generate music and sound effects; Stable Audio is one example.

There are two useful approaches here.

One is to generate audio and edit it. The other is to ask how to construct a sound using a synthesiser.

With VCV Rack, for example, a description of oscillators, filters, envelopes and modulation could become a practical patching exercise.

A generated audio file gives me a result to manipulate. Building the patch gives me control over how the sound behaves.

Both can be creatively useful.

9. Helping with orchestration: imagining who plays what

AI might suggest giving a melody to an oboe, placing sustained harmony in strings and adding a quiet horn response.

That can provide a starting point.

The musician then needs to consider range, balance, breathing, articulation and whether the parts make sense for the intended performers.

If I want real people to play the arrangement, I need to check it as a score. If I am producing it with virtual instruments, I still need phrasing and balance.

An orchestral sound library can make an awkward arrangement sound expensive. It cannot make every musical decision convincing.

10. Experimenting with styles: learning what changes

One short motif could become a waltz, a restrained electronic cue, a theatre-organ introduction or a brisk dance arrangement.

AI can suggest changes to metre, rhythm, harmony and texture.

For learning, I would describe musical features:

Keep the melody recognisable. Try triple metre, a gently moving bass and a light accompaniment.

That produces a more informative experiment than asking for a vague imitation.

The useful question is what changed in the music, and why that changed its character.

The Musician Still Decides What the Music Should Communicate

Consider three films containing the same boat:

1. A family enjoying an afternoon sail.

2. A racing crew approaching the start.

3. A documentary about the boat’s history.

The images share a subject, but the music has different work to do.

The family film may need ease and warmth. The racing sequence may need anticipation. The documentary may benefit from restraint, allowing voices and river sounds to carry much of the meaning.

Even “excitement” needs interpretation.

Do we want the audience to feel speed, tension, achievement or delight?

That decision shapes everything which follows.

For me, this is where musical work connects directly with filmmaking: understanding what the audience should notice and feel at each moment.

Rejecting a Good Track Can Be the Right Decision

A track may have an attractive melody and excellent production.

It may also be unsuitable.

Perhaps it competes with narration. Perhaps it sounds too solemn for a welcoming club video. Perhaps its large percussion makes a small business introduction feel absurdly dramatic.

This is why I would separate two questions:

Do I like this music?

Does it serve this project?

The answers do not always match.

If AI makes it easier to produce alternatives, we need a reliable way to choose between them. Otherwise, we can spend an afternoon generating variations without getting closer to a decision.

A clear brief helps us stop.

Phrasing, Timing and Instrumentation Give Music Its Character

A score records notes and durations, but performance involves decisions within them.

A slight hesitation before a phrase. A stronger first note. A softer answer. A bass line that moves forward while the melody seems to float.

AI can generate expressive performances too. The human contribution lies in deciding whether that expression communicates the intended meaning and changing it where necessary.

The same applies to instrumentation.

On the Pergamon, a melody can acquire a different character through registration, balance and articulation. A fuller combination of sounds may be exciting, yet a quieter registration may let the phrase speak more clearly.

That is one reason I remain interested in learning how sounds blend.

Having more sounds available makes listening carefully more valuable.

Matching Music to Pictures: A Practical Example

Imagine a proposed 30-second film of Champagne:

Time	Picture	Musical purpose
0–6 seconds	Quiet river and the boat at rest	Establish space and anticipation
6–14 seconds	Rigging and preparation	Introduce gentle movement
14–24 seconds	The boat sailing	Develop energy and the main idea
24–30 seconds	Closing image and title	Resolve clearly

A generated track might contain all the right ingredients but place them at the wrong times.

The strongest musical moment might occur during a close-up of a rope. The ending might arrive after the title has disappeared.

I could edit the audio, change the arrangement, record a new phrase or adjust the picture.

I might also decide that the opening needs only water, birds and the sound of preparation.

Music does not need to occupy every available second.

Try This: Three Versions, One Brief

A manageable experiment is more useful than generating twenty unrelated tracks.

Start with a brief:

Create music for a 20-second introduction to a welcoming sailing-club film. Convey curiosity and enjoyment. Leave room for one spoken sentence and finish clearly.

Then compare three approaches:

• a short phrase played yourself;

• the phrase developed using AI suggestions;

• an AI-generated audio version made from the brief.

Use the same pictures and keep playback levels reasonably similar. A louder version can seem more impressive simply because it is louder.

Ask:

• Does the emotion fit?

• Can I understand the speech?

• Does the music support the important picture changes?

• Is anything memorable?

• What would I change?

The aim is to discover which decisions improve the result.

Does Learning Music Still Matter?

Yes — because musical understanding gives us more precise ways to respond.

“This is wrong” becomes:

“The accompaniment is too busy.”

“The phrase needs room to breathe.”

“The bass movement weakens the ending.”

“The same motif should return with a different texture.”

You do not need advanced theory to begin. Listening, experimenting and describing what you hear are valuable starting points.

But greater knowledge expands your choices. It also helps you recognise when a tool has produced something plausible rather than something useful.

As a teacher, I would want AI to encourage that exploration. Ask for alternatives, play them, compare them and explain your decision.

AI Changes the Work — and Makes Judgement More Visible

It would be too comfortable to claim that every musical task will remain economically unchanged. Some clients will find a generated track adequate for their needs.

For musicians, that makes it useful to explain the value of work beyond producing an audio file: interpreting a brief, developing a recognisable identity, shaping a performance, responding to revisions and making music fit a particular production.

For client projects, I would also check the chosen tool’s current usage terms, permission for any uploaded material and how AI involvement should be described.

The creative standard remains straightforward:

Can I stand behind the result and explain why it belongs here?

Conclusion: More Music Makes Listening Matter More

AI can help us begin, explore, analyse and produce. It can offer an unexpected direction and make experimentation accessible to people who might otherwise never try composing.

That is exciting.

For my own interests — from the Pergamon and synthesisers to film, teaching and short musical identities — the attraction is the opportunity to explore more possibilities and understand them better.

But possibilities need purpose.

Someone must decide what the music should communicate, which ideas deserve development, how the phrases should move and when silence would do a better job.

The musician can be a performer, composer, editor, teacher and critical listener within that process.

If AI can generate thousands of pieces of music, musical judgement becomes one of the most valuable skills we can develop.

For a film, podcast or business introduction, the starting question is simple: what do you want your audience to feel?

Why Own a Waterproof Camera When Your Phone Is Waterproof?

 

Why Own a Waterproof Camera When Your Phone Is Waterproof?

Sometimes the photograph exists precisely because you were willing to get the camera wet.

There is a moment familiar to anyone who takes photographs outdoors. Something interesting happens, you reach for a camera, and then hesitate.

Perhaps rain is blowing across the sailing boat. Perhaps your hands are sandy. Perhaps the photograph would look much better from just above the water, but your expensive phone suddenly feels rather slippery.

By the time you have decided whether it is worth the risk, the moment has gone.

That is why waterproof cameras still have a useful place in my equipment collection. I have an older Olympus Tough TG-5 and a newer OM SYSTEM Tough TG-7, both in black.

Their appeal goes beyond surviving an accidental splash. They change the situations in which I feel comfortable taking photographs, whether I am sailing, exploring a beach or investigating tiny details in the garden.

First, Is Your Phone Actually Waterproof?

The word “waterproof” needs care, whether we are discussing phones or cameras.

Many phones have a water-resistance rating based on specified laboratory tests. That does not automatically mean their manufacturer recommends swimming with them or repeatedly immersing them in seawater.

Apple, for example, explains that water resistance can decrease with normal wear and advises against intentional submersion, swimming with an iPhone and exposure to high-velocity water. Other phones have their own conditions, so check the guidance for your particular model. Apple Support

The TG-7 is designed for underwater photography without a separate housing, within its stated limits: up to 15 metres deep and no longer than 60 minutes. Those limits depend on the camera being maintained correctly, with its covers securely closed and seals in good condition. my.omsystem.com

The useful question is therefore:

“Is this device designed for the way I intend to use it?”

Surviving an accidental dunk and being suitable for a planned underwater photography session are different requirements.

Rain Can Be Part of the Photograph

Rain encourages many photographers to put their equipment away. Unfortunately, that can mean missing some of the most interesting conditions.

Wet surfaces reflect light. Raindrops collect on ropes and leaves. Clouds soften the illumination, while a sudden shaft of sunshine can transform an otherwise grey scene.

For sailing photography, the weather is part of the story. A photograph of wet sails, dripping rigging and a determined crew can communicate the experience far better than another sunny view from the shore.

With a waterproof compact, I can make a different decision about taking the camera out in those conditions. There is less preparation between seeing something and photographing it.

Try this: photograph the same subject before, during and after a shower. A garden chair, boat fitting or leaf will do. Look for changes in reflections, texture and colour.

Check the front of the lens regularly. A waterproof camera can still produce a blurred photograph when a large droplet sits directly in front of it.

On a Sailing Boat, Practicality Matters

A sailing boat is a demanding place for photography.

The platform moves. Ropes need attention. Spray arrives unexpectedly. There may be nowhere convenient to put down a large camera.

With boats such as Champagne, the RS Toura and Whaly Coyote, I can see several useful roles for a small waterproof camera:

• Recording a crew’s view along the boat.
• Photographing a fitting before and after maintenance.
• Capturing details of sheets, blocks and rigging.
• Taking low viewpoints close to the water.
• Recording short clips of sailing techniques when someone else is safely handling the boat.

The TG-7 has a physical shutter button, which gives a clear control to locate when working quickly. Practise operating it with the gloves you actually wear; small buttons can still be awkward.

A suitable floating strap is also worth considering. Waterproof does not mean it will float. Check that the strap supports the camera and any attached accessories, and keep it clear of moving equipment.

Photography must fit around sailing. A good picture is never worth losing awareness of another boat or neglecting a manoeuvre.

Beaches and Mud: The Places Where You Hesitate

At the beach, water is only one concern.

Sand gets into bags, sticks to fingers and finds its way into small gaps. On a muddy walk, even reaching into a pocket can become an exercise in transferring dirt.

A rugged compact is useful because it suits these surroundings. You might photograph patterns left by a retreating tide, wet pebbles, a muddy boot or the intricate surface of a piece of driftwood.

There is a creative advantage here: you become more willing to get down to the subject’s level.

Instead of photographing a shell from standing height, you can crouch beside it and make the beach stretch away behind it. Instead of a general view of a puddle, you can explore the reflection from almost ground level.

The camera still needs care. Keep grit away from seals, and leave battery and card compartments closed until you are somewhere clean and dry.

Underwater Photography Begins in Shallow Water

You do not need to be a diver to find an underwater subject.

A clear rock pool or safe, accessible patch of shallow water can reveal a world that looks quite different from beneath the surface.

Useful subjects include submerged stones, aquatic plants, shells and the patterns of light moving over the bottom.

A simple starting method is:

1. Choose calm, clear water with secure access.
2. Use the camera’s appropriate underwater mode.
3. Start with a stationary subject in good daylight.
4. Move close enough to reduce the amount of water between camera and subject.
5. Take several frames and check them before moving on.

The newer TG-7 is designed for underwater photography without a separate housing, within its stated limits: up to 15 metres deep and no longer than 60 minutes underwater. Those limits depend on correct maintenance, securely closed covers and seals in good condition. An older waterproof camera, such as my TG-5, deserves particular attention to its present condition before being submerged. 

For both cameras, preparation and maintenance matter. Check the seals and compartment edges for contamination, close and lock the covers correctly, and follow the relevant manual’s freshwater cleaning and drying instructions after underwater use. Keep the compartments closed until the camera is clean and dry. With my older TG-5, I would pay particular attention to seal condition rather than assume that its original waterproof rating guarantees its condition today.

The Surprise Benefit: Remarkable Close Focusing

For me, close-up photography is an important part of the argument for owning a waterproof compact.

My newer TG-7 can focus as close as approximately 1 cm from the front of the lens in microscope mode. It also offers focus stacking, taking photographs focused at different distances and combining them to show more of a stationary subject sharply. learning.omsystem.com

That opens up subjects such as rope fibres, corrosion on a boat fitting, leaf veins, shell ridges and droplets on moss.

This connects photography with my interest in science and teaching. A familiar object becomes a starting point for questions.

Why does that leaf hold droplets? How is the rope constructed? What does corrosion actually look like close up?

Try this: take three photographs of one object: its surroundings, the whole object and its smallest interesting detail. Together, they tell a much richer story.

At very close distances, avoid shading the subject with the camera. For focus stacking, choose something still and support the camera; movement can spoil the combined result.

Does It Take Better Photographs Than a Phone?

Sometimes a phone will produce the more pleasing image with less effort, particularly when its automatic processing handles a difficult scene well. It is also convenient for editing and sharing.

A larger camera offers its own advantages, including lens choice and greater scope for demanding photographic work.

The waterproof compact earns its place through a combination of access, handling and specialist features. The TG-7 also offers a 4× optical zoom and RAW recording for those who want more editing flexibility. OM SYSTEM

There is another practical consideration: my phone has several jobs.

It is a communication device, a source of information and potentially a means of calling for help. Keeping it protected while using a separate camera can be a sensible arrangement.

The compact has its own battery too. A photography session need not consume the phone power I may want later.

Waterproof Equipment Still Needs Looking After

Confidence comes from preparation and maintenance.

For the TG-7, the manufacturer’s guidance includes checking for dirt and sand, securely locking the compartment covers and keeping them closed in wet or dusty surroundings. After seawater use, it specifies a freshwater soak of about ten minutes with the covers locked and the lens ring removed, followed by drying in a shaded, ventilated place. Follow the full manual for the procedure. my.omsystem.com

An older camera deserves particular attention. Do not assume that its original depth rating proves the present condition of its seals.

Before a planned underwater session, check its condition and maintenance requirements. If there is damage or uncertainty, have it assessed.

A Small Camera Can Expand Your Photography

The photographic value of the TG-7 is easy to underestimate when comparing specifications indoors.

Its purpose becomes clearer beside a wet boat, on a beach or in a muddy garden. These are places where convenience and confidence influence whether the camera comes out at all.

For my mixture of sailing, photography and practical science, that makes a waterproof compact a useful tool. It encourages lower viewpoints, closer observation and photographs in conditions I might otherwise avoid.

The best result may be a dramatic sailing picture. It may be a tiny detail on a leaf. It may simply be a useful photograph of a boat fitting before I take it apart.

Sometimes the photograph exists precisely because you were willing to get the camera wet.

Make a Video — One Skill, One Short Film at a Time: Week 2 What Should the Viewer Notice?

 

What Should the Viewer Notice?

Make a Video — One Skill, One Short Film at a Time: Week 2

Your camera records everything inside the frame. Your job is to decide which part matters.

Last week, we began with three shots: wide, medium and close-up. Filming someone opening a notebook showed how to establish a setting, show an action and draw attention to a detail.

This week, we will keep that simple subject and ask a different question:

When someone watches this shot, what should they notice first?

The notebook? The person opening it? The words on the page? Or the colourful shopping bag, bright window and tangled cables behind them?

A picture can be sharp and correctly exposed yet still be confusing. If several things compete for attention, the viewer has to work out which one matters.

This week’s skill is learning to make that choice for them.

The Frame Is Your First Storytelling Decision

Framing means deciding what appears inside the picture, where it appears and what you leave outside.

Imagine filming a person opening a notebook before beginning a project.

In one version, they are a small figure at the far end of a busy room. We notice the room before we notice the action.

In another, their hands and notebook occupy most of the picture. We immediately understand that opening the notebook matters.

Neither framing is automatically wrong. The first could introduce the workspace; the second could introduce the task.

A useful shot is one whose composition supports its purpose.

Before recording, finish this sentence:

“I want the viewer to notice…”

For this week’s activity, a useful answer is:

“I want the viewer to notice the hands opening the notebook.”

That gives you a clear reason for every adjustment you make.

What You Need

• A phone or camera.
• A notebook and someone to open it.
• A table and a safe place to support the camera.
• A few everyday objects to create a busy background.
• Around 20 minutes.

Use your phone’s normal video setting. Choose landscape or portrait according to where you intend to show the video, and keep that orientation throughout the exercise.

You do not need an expensive lens, a studio or editing software. We are practising decisions you can make before pressing record.

Get Filming: One Subject, Three Different Versions

Record three short clips of the same action. Aim for around eight to ten seconds each, leaving a brief pause before and after the notebook opens.

Keep the action, camera height and lighting as consistent as practical. We want to see what changes when you alter the background and framing.

Version 1: Record the Scene as You Find It

Place the camera where you might naturally put it and film the notebook opening.

For this exercise, leave some ordinary clutter in view: perhaps a mug, a bright bag or a few cables. Check that no private information is visible.

Play the clip back immediately.

Ask:

• What do I notice first?
• Can I clearly see the notebook opening?
• Does anything pull my attention away?

Do not rush to label this a bad shot. It is your starting point, and it gives you something useful to compare.

Version 2: Change the Background

Keep the camera and notebook in approximately the same positions. Now remove or reposition two or three distractions.

Move the bright bag out of view. Put loose cables aside. If a mug overlaps the hands, move it to the edge of the table or remove it.

Record the same action again.

A background does not have to be empty. A pencil beside the notebook might help explain that someone is about to write. A stack of unrelated packaging probably does not.

Keep details that support the story. Reconsider details that compete with it.

Version 3: Change the Framing

Use the improved background, then move the camera closer or choose a slightly different angle.

Try framing the hands, notebook and enough table to make the action easy to understand. Leave room for the notebook to open fully without the important movement disappearing beyond the edge.

Record the action again.

Notice how changing the camera position can exclude distractions without moving everything in the room. You are choosing a more useful view of the same place.

For this first exercise, move the camera rather than relying on digital zoom. Move carefully, and keep it securely supported.

Compare the Three Clips

Watch them in order:

1. The scene as you found it.
2. The same framing with a simpler background.
3. The simpler background with more deliberate framing.

If possible, show them to someone without explaining your intention first. Ask:

“What did you notice first in each clip?”

Their answer is more useful than simply asking which clip they liked.

This week’s success is three comparable clips and one clear explanation of why your chosen version works better.

Why Backgrounds Matter More Than We Think

When we look at a scene directly, we already know what interests us. It is easy to overlook the things around it.

The camera includes those things too.

Bright areas, strong colours, readable signs and unexpected movement can all compete with the subject. A television behind an interviewee may draw attention because the picture keeps changing. A bright window may dominate a shot even when the person is the intended focus.

Background shapes can also create awkward overlaps. A lamp or shelf edge behind someone’s head may become distracting once you watch the recording.

Before filming, scan the whole frame.

Look at the corners, the space behind the subject and the area into which the action will move. Sometimes shifting the camera a few centimetres is enough to improve the shot.

Make the Subject Easy to Find

There are several simple ways to direct attention. Try one at a time so you can see what each change achieves.

Give the Subject Enough Space in the Picture

If the notebook is the important object, it needs to be large enough to understand.

A close shot can reveal fingers lifting the cover or a pen touching the page. But moving too close can remove useful context or cut off the action.

Choose the distance that makes the important moment clear.

Use Position with a Purpose

The centre is often a sensible place for a beginner’s main subject. A straightforward shot of hands opening a notebook may work perfectly with the notebook centred.

Placing a subject to one side can also be useful, particularly when you need room for another object, a person’s gaze or a later caption.

The rule of thirds is a helpful experiment: imagine two evenly spaced vertical and horizontal lines dividing the frame into nine rectangles, then try placing the subject near a line or intersection.

Treat it as an option. A clear centred composition is better than an awkward arrangement made only to follow a rule.

Separate the Subject from Its Surroundings

A dark notebook may be difficult to distinguish on a dark table. A lighter surface underneath can make its outline easier to see.

Likewise, shifting a person away from a busy background may reduce overlaps and help the shot feel clearer.

You can explore background blur later. For now, practise creating separation through position, colour and a simpler view.

Leave Room for Movement

A frame must work throughout the action.

Check what happens when the cover opens, the hands move or the person reaches for a pen. Something that fits neatly into the first frame may disappear from view halfway through the clip.

Rehearse the movement once while watching the screen before you record.

Three Practical Examples from My Work

Across teaching, music and sailing videos, the question remains the same: what does the viewer need to see at this moment?

A Science Demonstration

A wide view of a laboratory can establish the setting. When a colour change becomes the important moment, the liquid needs to be clearly visible.

A plain background behind suitable glassware can make that change easier to distinguish. Nearby bottles and labels may be useful context, but they can also compete with the result.

The framing should let the viewer see the evidence that the explanation depends on.

An Organ Performance

A view of the performer and instrument introduces the performance. A closer view of the keyboards, hands or pedalboard reveals a particular technique.

If the purpose is to show a passage played on the pedals, that movement needs enough space in the picture to be understood.

The best view depends on whether the video is presenting the music, demonstrating a skill or doing both.

A Sailing Video

A wide shot can show the fleet and the course. A closer view can help explain a crew movement or sail adjustment.

On the river, you cannot tidy the entire background. You can choose your position, angle and timing so that the relevant boat or action is easier to follow.

That makes this week’s exercise especially useful: sometimes you improve a picture by moving objects; sometimes you improve it by changing your view.

A Teaching Reflection: Make One Decision at a Time

With my teaching experience and access to a range of filming equipment, I can see how easily a simple activity could become overloaded.

We could discuss lenses, exposure, lighting, camera movement and editing. Each has its place. But for someone beginning to make videos, that can turn one manageable task into several unfamiliar problems.

For this series, I want each week to produce something small that a reader can complete and recognise as progress.

This week, that might mean spotting a distracting bag, moving it, and recording a clearer shot. It might mean moving the camera closer and discovering that the action suddenly makes sense.

Those are useful filmmaking decisions.

A learner does not have to explain every technical setting to say:

“This version works better because I can see what the hands are doing.”

That is understanding we can build on.

Pro Tips: Small Changes That Make a Difference

• Watch on a larger screen if you can. Distractions that are hard to spot on a phone may become obvious.
• Change one thing, then compare. You will learn more easily when you know what caused the improvement.
• Check focus after moving the camera. Confirm that the important hands or object remain sharp; test any focus-lock control your device provides.
• Give the action breathing room. Allow a moment before it starts and after it finishes.
• Keep useful context. A completely bare setting may remove information the viewer needs.
• Look around the edges. Stray objects and awkward cuts often appear there.
• Make a quick test recording. Playback is a better check than assuming the view on the screen will work.

Optional Extension: Tell Two Stories with the Same Notebook

Once you have completed the basic activity, try two different intentions.

Story A: Someone is starting a productive morning.

Include a pen, a clear writing space and the hands opening the notebook.

Story B: Someone has discovered an important detail.

Frame a finger pointing to a particular line on a page. Use invented text, and make it large enough to read if the words themselves matter.

The object has stayed the same, but your choices tell the viewer to notice something different.

This is where composition begins to become storytelling.

Before You Finish

Choose your clearest clip and complete these three sentences:

• “I wanted the viewer to notice…”
• “I changed…”
• “The change helped because…”

There is no requirement to publish it. Save the clips so you can compare your work as the series develops.

A Better Video Begins with a Clearer Intention

Last week, we learnt to show a subject at three different shot sizes. This week, we have learnt to give a shot a clear purpose.

A stronger video can begin with a modest change: a distraction moved aside, a better camera position or a little more room for the action.

Before your next recording, pause and ask:

“What should the viewer notice?”

Then look at the frame and make sure you have given them a fair chance to notice it.

You can find a lot more details on the website that accompanies the series at https://makingbetter.video/

Next Sunday: High, Low or Eye Level? — How Camera Height Changes the Story