computer science

QOI (image format)

QOI (image format) is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand QOI (image format) rather than just read about it. In short: The Quite OK Image Format (QOI) is a specification for lossless image compression of 24-bit (8 bits per color RGB) or 32-bit (8 bits per color with 8-bit alpha channel RGBA) color raster (bitmapped) images, invented by Dominic Szablewski and first announced on 24 November 2021. Description It is an open source lossless compression method that is faster and easier to implement than PNG.

Key takeaways

QOI (image format) belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect QOI (image format) to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of QOI (image format) from memory before moving on to harder problems.

Reference excerpt

The Quite OK Image Format (QOI) is a specification for lossless image compression of 24-bit (8 bits per color RGB) or 32-bit (8 bits per color with 8-bit alpha channel RGBA) color raster (bitmapped) images, invented by Dominic Szablewski and first announced on 24 November 2021.

Description It is an open source lossless compression method that is faster and easier to implement than PNG. Figures specified in the blog post announcing the format claim 20-50x faster encoding and 3-4x faster decoding speed compared to PNG, both with similar file sizes. The author has donated the specification to the public domain (CC0). In 2022, researchers from the École Polytechnique Fédérale de Lausanne verified that the compression is lossless.

Software and language support QOI is supported by FFmpeg (v5.1+), GIMP (v3.0+), GraphicConverter (v11.8+), ImageGlass (v8.5+, read-only), ImageMagick (v7.1.0-20+), Imagine (v1.3.9+), and IrfanView (v4.60+, with plugin). Microsoft PowerToys (v0.76+) for Windows 10 and 11 adds support for previewing QOI images to File Explorer. Community made plugins are available in GIMP, Paint.NET and XnView MP. The game engine GameMaker has used a combination of bzip2 and QOI as the default storage format for texture groups since version 2022.1.0.609. Despite being smaller, files in the format decompress faster than those in the PNG format it displaced. The engine also offers plain QOI for increased decompression performance, and PNG for compatibility with tooling and web platforms. There are also implementations for various languages such as Rust, Python, Java, C++, C# and more. A full list can be found on the project's Git(Hub) repository README.

File format

Header A QOI file consists of a 14-byte header, followed by any number of data “chunks” and an 8-byte end marker.

The colorspace and channel fields are purely informative. They do not change the way data chunks are encoded.

Encoding Images are encoded row by row, left to right, top to bottom. The decoder and encoder start with {r: 0, g: 0, b: 0, a: 255} as the previous pixel value. An image is complete when all pixels specified by width * height have been covered. Pixels are encoded as:

Run-length encoding of the previous pixel (QOI_OP_RUN) an index into the array of previously seen pixels (QOI_OP_INDEX) a difference compared to the previous pixel value in r,g,b (QOI_OP_DIFF or QOI_OP_LUMA) Full r,g,b or r,g,b,a values (QOI_OP_RGB or QOI_OP_RGBA) The color channels are assumed to not be premultiplied with the alpha channel (“un-premultiplied alpha”). A running array[64] (zero-initialized) of previously seen pixel values is maintained by the encoder and decoder. Each pixel that is seen by the encoder and decoder is put into this array at the position formed by a hash function of the color value. In the encoder, if the pixel value at the index matches the current pixel, this index position is written to the stream as QOI_OP_INDEX. The hash function for the index is:

Each chunk starts with a 2- or 8-bit tag, followed by a number of data bits. The bit length of chunks is divisible by 8 - i.e. all chunks are byte aligned. All values encoded in these data bits have the most significant bit on the left. The 8-bit tags have precedence over the 2-bit tags. A decoder must check for the presence of an 8-bit tag first. The byte stream's end is marked with 7 0x00 bytes followed by a single 0x01 byte. The possible chunks are:

QOI_OP_RGB

8-bit tag b11111110 (254) 8-bit red channel value 8-bit green channel value 8-bit blue channel value The alpha value remains unchanged from the previous pixel.

QOI_OP_RGBA

8-bit tag b11111111 (255) 8-bit red channel value 8-bit green channel value 8-bit blue channel value 8-bit alpha channel value

QOI_OP_INDEX

2-bit tag b00 6-bit index into the color index array: 0..63 A valid encoder must not issue 2 or more consecutive QOI_OP_INDEX chunks to the same index. QOI_OP_RUN should be used instead.

QOI_OP_DIFF

2-bit tag b01 2-bit red channel difference from the previous pixel -2..1 2-bit green channel difference from the previous pixel -2..1 2-bit blue channel difference from the previous pixel -2..1 The difference to the current channel values are using a wraparound operation, so 1 - 2 will result in 255, while 255 + 1 will result in 0. Values are stored as unsigned integers with a bias of 2. E.g. −2 is stored as 0 (b00). 1 is stored as 3 (b11). The alpha value remains unchanged from the previous pixel.

QOI_OP_LUMA

2-bit tag b10 6-bit green channel difference from the previous pixel -32..31 4-bit red channel difference minus green channel difference -8..7 4-bit blue channel difference minus green channel difference -8..7 The green channel is used to indicate the general direction of change and is encoded in 6 bits. The red and blue channels (dr and db) base their diffs off of the green channel difference. I.e.:

The difference to the current channel values are using a wraparound operation, so 10 - 13 will result in 253, while 250 + 7 will result in 1. Values are stored as unsigned integers with a bias of 32 for the green channel and a bias of 8 for the red and blue channel. The alpha value remains unchanged from the previous pixel.

QOI_OP_RUN

2-bit tag b11 6-bit run-length repeating the previous pixel The run-length is stored with a bias of −1. Note that the runlengths 63 and 64 (b111110 and b111111) are illegal as they are occupied by the QOI_OP_RGB and QOI_OP_RGBA tags.

History Dominic Szablewski, the developer, recognized that traditional file formats were complex, economically motivated, and paywalled, while other file formats were still incredibly complex or proprietary. He felt that there was a need to create a simpler file format that reduced compression ratio for performance, so he sought out initially creating a new video codec, instead creating an early version of QOI. While the file format was undergoing scrutiny from GitHub users, the creator actively rejected suggestions that made the format more complex. There will be no more improvement done to the file format, as it has been finalized.

References

External links Format website: C Source code and benchmark results 1 page PDF specification GitHub repository (including C implementation) How PNG Works: Compromising Speed for Quality - YouTube A video comparing compression techniques in PNG and QOI with animations and examples.

Worked examples

Example 1 — a first encounter with QOI (image format)

Start with the simplest possible case. Write down what QOI (image format) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to QOI (image format) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about QOI (image format) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of QOI (image format)

In research: QOI (image format) appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses QOI (image format) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: QOI (image format) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computer-related introductions in 2021, Graphics standards, Image compression, so understanding it makes those chapters shorter.
In everyday life: Look for QOI (image format) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study QOI (image format) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what QOI (image format) means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain QOI (image format) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is QOI (image format) in simple terms?

Why does QOI (image format) matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study QOI (image format)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on QOI (image format).