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Image Histogram

A histogram counts how many pixels sit at each brightness level, shadows on the left and highlights on the right. Bars stacked against either edge mean detail has been clipped to pure black or pure white and cannot be recovered.

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How to read an image histogram

  1. Add your image

    Drop a JPG, PNG or WebP onto the drop zone, or click to choose one.

  2. Look at the shape

    Left is shadows, right is highlights. A spike hard against either edge means clipped detail.

  3. Switch channels

    Luminance shows overall exposure. RGB overlays all three so you can spot a colour cast or a single clipped channel.

  4. Check the numbers

    Clipped percentages, average brightness and the tonal range actually used are listed under the chart.

Why use this tool

Luminance and per-channel views

Switch between weighted luminance, an RGB overlay, or any single channel on its own.

Clipping measured, not guessed

The exact percentage of pixels at pure black and pure white is reported, and turns red past one percent.

Tonal range used

Shows the darkest and lightest values actually present, which tells you whether an image is using its full range or sitting flat.

Logarithmic option

A log scale reveals small counts that a single huge spike would otherwise flatten into invisibility.

Runs entirely in your browser

Your image is never uploaded.

About this tool

A histogram is a count, nothing more. For each of the 256 possible brightness values it tallies how many pixels in the image have that value, then draws the result as a chart with the darkest values on the left and the brightest on the right. The shape tells you how the tones are distributed, and the tall spikes tell you where most of the image sits.

What makes it worth reading is clipping. If a large number of pixels have landed on exactly 0 or exactly 255, that detail is gone: the shadows are solid black or the highlights are blown to white, and no amount of editing can recover something that was never recorded. Those two edges are the first place to look, which is why the exact percentage is reported here rather than left to your judgement of the chart.

The luminance view weights the channels the way the eye does, at roughly 21 percent red, 72 percent green and 7 percent blue, so it reflects perceived brightness rather than raw numbers. The RGB overlay is where colour problems show up: a channel shifted left or right of the others indicates a colour cast, and one channel clipping alone is common in saturated reds and skies. The logarithmic option is useful when a flat background produces one enormous spike that squashes everything else flat. For related work see image color count, image color palette, and grayscale image.

Frequently asked questions

What does an image histogram show?
How many pixels sit at each brightness level, from pure black on the left to pure white on the right. The height of the chart at any point is the number of pixels with that value.
What does clipping mean?
Pixels pushed all the way to 0 or 255, where detail no longer exists. A spike hard against the left or right edge is clipped, and that information cannot be edited back in.
Is there a correct histogram shape?
No. A low-key photograph should be weighted left and a high-key one right. The shape is only wrong if it does not match what you intended, or if it is clipping detail you wanted.
Why use luminance instead of a plain average?
Because the eye is far more sensitive to green than to blue. Luminance weights the channels at about 21, 72 and 7 percent, which matches perceived brightness. A flat average would misrepresent it.
When should I use the logarithmic scale?
When one value dominates so heavily that everything else is flattened, which is common with large flat backgrounds. The log scale compresses the spike so the smaller counts become visible.
Is my image uploaded anywhere?
No. The image is read in your browser and never leaves your device.

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