#+TITLE: ImageSqueeze - lossy image codec
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- [[http://www2.svjatoslav.eu/gitweb/?p=imagesqueeze.git;a=snapshot;h=HEAD;sf=tgz][download latest snapshot]]
- This program is free software; you can redistribute it and/or modify it under
the terms of version 3 of the [[https://www.gnu.org/licenses/lgpl.html][GNU Lesser General Public License]] or later as
published by the Free Software Foundation.
- Program author:
- Svjatoslav Agejenko
- Homepage: http://svjatoslav.eu
- Email: mailto://svjatoslav@svjatoslav.eu
- [[http://www.svjatoslav.eu/programs.jsp][other applications hosted at svjatoslav.eu]]
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* Overview
Lossy image codec. Optimized for photos. I developed it to test out an
image compression ideas.
I believe my [[id:1d917f74-e763-4a71-976e-4aa60732efa6][algorighm has interesting advantages.]]
Below are original photo and the same image being compressed down to
~93 Kb and then decompressed.
[[file:originalAndCompressed.png][file:originalAndCompressed.png]]
When looking very closely, slight grainyness, loss of color precision
and blurriness (loss of detail) could be noticed as a compression
artifacts. Still sharp edges are always preserved. Also no blocks
typical to JPEG are ever seen. I think that is awesome result for
just ~ 2.5 bits per pixel on that color photo.
* Algorithm description
+ Color image is split into three separate channels: Y, U and V.
+ Each channel is independently compressed.
+ Single channel compression method:
+ Algorithm is inspired by [[https://en.wikipedia.org/wiki/Diamond-square_algorithm][diamond-square algorithm]] that is meant to
produce random heightmap/plasma effect.
+ During image compression: 2D image surface is iterated in the
similar manner to diamond-square algorithm. Average color from
neighbors is calculated and difference between neighbors average
and actual pixel color is saved. As the pixel grid becomes
gradually more dense, difference between neighbors tends to get
smaller, thereby requiring less bits per pixel for storing the
difference.
** Algorighm advantages
:PROPERTIES:
:ID: 1d917f74-e763-4a71-976e-4aa60732efa6
:END:
+ It can be applied to any amount of dimensions, even for sound and
volumetric data.
+ Algorithm can operate in lossy and lossless mode.
+ Algorithm naturally handles progressive loading. That is: low
resolution thumbnail of entire thing is immediately available and
gets gradually more dense during entire loading process.
+ Algorithm naturally supports variable resolution. That is: different
areas can be encoded with different resolutions / pixel densities.
+ Fast: Very little computations per pixel.
* TODO Things to improve
- Code documentation is weak.
- Better sample applications needed:
- Commandline image conversion utility.
- Image viewer.
- Add lossless support.
- Extract algorithm key parts into reusable library and make it
variable dimensional. So that the same code can be used for sound,
image and volumetric data compression/decompression.