RGB Histogram Viewer

A histogram is a graphical representation of the tonal values in your image. It shows the distribution of pixels across brightness levels from black (0) on the left to white (255) on the right. The height at each point indicates how many pixels exist at that brightness level. The left side represents shadows, the middle represents midtones, and the right side represents highlights.

If the graph touches or climbs the right edge of the frame (value 255), it indicates 'clipping' in the highlights. This means those pixels are pure white and contain no recoverable detail. This is often called 'blown out' highlights. Similarly, if the graph climbs the left edge, you have 'crushed blacks' with no shadow detail.

An RGB histogram shows the distribution of the three individual color channels (Red, Green, Blue) overlaid on each other, letting you see color-specific exposure issues. A Luminance histogram shows perceived brightness using a weighted formula (R×0.299 + G×0.587 + B×0.114) that mimics how the human eye perceives brightness—we see green as brighter than blue.

Linear scale shows the raw pixel count, which is great for general exposure analysis. However, if you have very few pixels in a certain tonal range (like stars in a night sky or subtle highlights), they might be invisible on a Linear graph. Logarithmic scale compresses the vertical axis, revealing these subtle, low-frequency details that would otherwise be hidden.

No. This tool runs entirely in your browser using client-side JavaScript and HTML5 Canvas. Your photos are processed locally on your device and are never uploaded to any server, ensuring 100% privacy and instant analysis speed. The tool even works offline once the page has loaded.

Clipping occurs when pixels are too bright (highlight clipping at 255) or too dark (shadow clipping at 0) to contain any detail. On a histogram, it appears as a spike pressed against the edges. Clipping is important because clipped data cannot be recovered in post-processing—the information is permanently lost. Always 'expose to the right' without clipping to maximize image quality.

There's no single 'correct' histogram shape—it depends on your subject. A high-key image (snowy scene) naturally peaks to the right. A low-key image (night scene) peaks to the left. A standard daylight photo typically has a 'bell curve' distribution with most tones in the midtones and tapered edges. What matters is that your histogram matches your creative intent without unwanted clipping.

Because most real-world scenes aren't perfectly neutral gray. A sunset will have a Red channel shifted right (bright) and Blue channel shifted left (dark). A forest scene will have an elevated Green channel. Analyzing channel differences helps you identify unwanted color casts and understand your image's color distribution.

Midtones are brightness levels in the middle range (roughly 64-192 in 8-bit color). This is where most subject matter lives—skin tones, foliage, architecture, and everyday objects. Ensuring good contrast and proper exposure in the midtones is key to creating images that 'pop' with depth and dimension.

Before shooting, use your camera's live histogram to adjust exposure. After shooting, analyze your photos to learn your camera's behavior and identify consistent issues. Over time, you'll develop an intuition for proper exposure. Use the histogram to understand why some of your photos look better than others and replicate those conditions.

While this tool is designed for still images, the principles are identical for video. Many photographers and videographers analyze representative frames from their footage using tools like this to understand the tonal range before color grading in dedicated video software.

Mean is the average brightness of all pixels—useful for overall exposure. Median is the middle value when all pixels are sorted—less affected by extreme highlights or shadows. If Mean is significantly higher than Median, you have bright outliers (highlights). If Mean is lower, you have dark dominant areas. Comparing these helps you understand your image's tonal distribution.