A FRESH NEW LOSSLESS IMAGE FORMAT

Launch date: 3rd October, 2015Latest Release: 22nd September, 2016

FLIFLossless FLIF

FLIF is a novel lossless image format which outperforms PNG, lossless WebP, lossless BPG, lossless JPEG2000, and lossless JPEG XR in terms of compression ratio.

Lossy FLIFIn developer’s opinion, at low qualities and for photographs, dedicated lossy formats like WebP, JPEG or BPG still produce better results. However, at very high-quality, we think lossy FLIF is a better option. 

WHY FLIF JPEG most popular right now. However JPEG-

Is lossy trade off between the file size and the loss of visual quality no alpha channel support (doesn't support semi or fully transparent

pixels)

PNG can be used to overcome disadvantages of JPEG. But- generated files are huge compared to lossy bandwidth consumed is large

Finding a way to save images without compromising on quality while having the smallest possible file, is definitely important for the modern web and mobile world

FLIF is the winnerAccording to the compression experiments, FLIF(lossless) files are on average:

14% smaller than lossless WebP,22% smaller than lossless BPG,33% smaller than brute-force crushed PNG files (using ZopfliPNG),43% smaller than typical PNG files,46% smaller than optimized Adam7-interlaced PNG files,53% smaller than lossless JPEG 2000 compression,74% smaller than lossless JPEG XR compression.

Works on any kind of imageFLIF does away with knowing what image format performs the best at any given task.

PNG works well for line art, but not for photographs. For regular photographs where some quality loss is acceptable, JPEG can be used, but for medical images you may want to use lossless JPEG 2000. And so on. It can be tricky for non-technical end-users.

More recent formats like WebP and BPG do not solve this problem, since they still have their strengths and weaknesses.

FLIF works well on any kind of image, so the end-user does not need to try different algorithms and parameters.

The conclusion? FLIF beats anything else in all categories.

Progressive and losslessFLIF is lossless, but can still be

used in low-bandwidth situations, since only the first part of a file is needed for a reasonable preview of the image.

Other lossless formats also support progressive decoding (e.g. PNG with Adam7 interlacing), but FLIF is better at it. Here is a simple demonstration video, which shows an image as it is slowly being downloaded:

Here’s a video example(if the video does not play, you can watch it here: https://youtu.be/ByH7RMsMxBY)

Generation Loss(For Lossy FLIF)Generation loss is the loss of

quality between subsequent copies or transcodes of data.

One advantage (of many) of using a lossless format in a lossy way (as opposed to using a lossy format), is that generation loss is not an issue.

Here’s a video example(if the video does not play, you

can watch it here: https://youtu.be/gJJachY651c)

Technical Information

FLIF is based on MANIAC compression. MANIAC (Meta-Adaptive Near-zero Integer Arithmetic Coding) is an algorithm for entropy coding developed by Jon Sneyers and Pieter Wuille.

It is a variant of CABAC (context-adaptive binary arithmetic coding), where instead of using a multi-dimensional array of quantized local image information, the contexts are nodes of decision trees which are dynamically learned at encode time. This means a much more image-specific context model can be used, resulting in better compression.

Tech Info contd (MNIAC)This entropy encoding method is called “meta-

adaptive near-zero integer arithmetic coding” (MANIAC). It is meta-adaptive since the context model itself is adapted to the data

Proposed is a dynamic data structure as a context model. It is essentially a decision tree (actually one tree per channel), grown during encoding. Figure shows an example MANIAC tree. Every internal (non-leaf) node has a test condition: an inequality comparing one of the context properties to a value. The child nodes correspond to the two test branches. During encoding, every leaf node contains one actual context (array of chances) and two virtual contexts per property. At decode time only the actual contexts are used.

Technical Information

Moreover, FLIF supports a form of progressive interlacing (essentially a generalization/improvement of PNG's Adam7 interlacing) which means that any prefix (e.g. partial download) of a compressed file can be used as a reasonable lossy encoding of the entire image. In contrast to other interlacing image formats (e.g. PNG or GIF), interlaced FLIF encoding takes the interlacing into account in the pixel estimation and in the MANIAC context model. As a result, the overhead of interlacing is small, and in some cases (e.g. photographs) interlaced FLIF files are even smaller than non-interlaced ones.

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