-I believe my algorithm has following advantages:
- * Fast. Relatively few computations per pixel.
- * Easy to add support for progressive image loading. (Saving is already progressive)
+
-Below are original photo and the same image being compressed down to ~93 Kb and then decompressed.
-
+
+
2 Overview
+
+
+Lossy image codec. Optimized for photos. I developed it to test out an
+image compression ideas.
+
-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.
-
+
+Below are original photo and the same image being compressed down to
+~93 Kb and then decompressed.
+
+
+
+
+
+
+
+
+
+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.
+
+
+
+
+
3 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 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.
+
+
+
+
+
+
+
3.1 Algorighm advantages
+
+
+
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.
+
+
+
+
+
+
+
+
4TODO 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.
+
+