Image Splitter Memory: Calculate Pixel Buffer Size

Why a small file can contain a large image

A PNG, JPG, or WebP file stores encoded image data. Its size depends on content and encoding. When you work with decoded pixels, the dimensions matter separately: an image of a flat background and a photograph can have the same dimensions but very different file sizes.

The calculation on this page refers specifically to an RGBA8 pixel array: four channels with one byte each. Browser memory is more complicated than one such array. Internal canvas surfaces, caches, graphics memory, and alternate pixel formats can differ. Treat this calculation as a clearly defined piece of the workload, rather than an exact memory meter or a promise that your device can process the image.

MDN's ImageData documentation describes RGBA channel order and the distinction between 8-bit and floating-point pixel arrays. The examples here use the 8-bit case only.

A calculated pixel-buffer table

These values are independently calculated from dimensions, not measured browser peaks. First multiply width by height to get the pixel count, then multiply by four for one RGBA8 array. Divide bytes by 1,048,576 to express the result in MiB. Decimal MB and binary MiB use different divisors; the table keeps the original byte count visible so you can reproduce every conversion.

For example, 6000 × 4000 × 4 = 96,000,000 bytes. Dividing by 1,048,576 gives 91.552734375 MiB, rounded to 91.6 in the table. In decimal units the same buffer is 96 MB. Neither number tells you the compressed PNG or JPG download size.

Halving both dimensions makes a quarter as many pixels: 3000 × 2000 is 6,000,000 pixels, compared with 24,000,000 for 6000 × 4000. The corresponding RGBA8 array falls from 96,000,000 bytes to 24,000,000 bytes. This is why reducing dimensions can change the working pixel budget much more directly than adjusting a compression quality slider.

Splitting into six pieces does not remove the source workload

Suppose you split a 6000 × 4000 image into 2 rows and 3 columns. Each piece is 2000 × 2000. A single piece has 4,000,000 pixels, corresponding to 16,000,000 bytes or about 15.3 MiB as an RGBA8 array. Six such pieces account for all 24,000,000 source pixels.

CutMyImage processes tile exports in a sequence, rather than deliberately creating all tile canvases at once. However, it retains exported blobs and object URLs for downloads and previews. Those encoded blobs are not interchangeable with the raw pixel arrays in this table. The browser also controls when temporary objects and graphics resources are released.

Consequently, do not claim that this example requires exactly 192,000,000 bytes of RAM. The useful conclusions are narrower: splitting changes each piece's dimensions; it does not automatically resize the input; and the source plus retained results can keep more than one type of image data alive.

If your intended output is a small set of slides, consider whether you need the full source resolution. Resize a copy before splitting when lower resolution is acceptable, preserving the original separately. If you need the original pixels for print or further editing, smaller dimensions may be the wrong trade-off.

Calculate your own RGBA8 buffer

Enter the source width and height in pixels. This calculator performs arithmetic only: it does not upload a file, decode an image, allocate a canvas of that size, or test your device's limits. Its output is the size of one hypothetical RGBA8 pixel array.

To make a small, reproducible pixel-array check in your own browser console, use new ImageData(320, 200).data.byteLength. With the default 8-bit format, it returns 256,000 bytes: 320 × 200 × 4. That is the array's storage, not the memory of the whole tab. A compact example is sufficient to check the arithmetic; there is no need to allocate a huge test image.

Use a smaller workload when a split stalls

1. Check the actual pixel dimensions, then use the calculator above. Do not substitute file size for width and height.
2. Try one source image instead of a whole batch. This separates a single-image workload from accumulated results.
3. If appropriate for the output, make a lower-resolution copy and split that. Keep the full-resolution original.
4. Download results you need before clearing them. CutMyImage revokes its result object URLs when results are cleared; this is cleanup, not a guarantee of an immediate measured drop in RAM.
5. If you still cannot complete the split, stop increasing the workload. Record dimensions, format, and the stage that failed, rather than repeatedly queueing more images.

These are workload controls, not a diagnosis of every failure. A tab can also fail for reasons unrelated to this simple pixel-array budget. No universal maximum image dimension, device memory requirement, or guaranteed completion time is asserted here.