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A New Operating Tool for Coding in Lossless Image Compression

A New Operating Tool for Coding in Lossless Image Compression. Radu Rădescu University POLITEHNICA of Bucharest, Faculty of Electronics, Telecommunications and Information Technology Department of Applied Electronics and Information Engineering OC 2009, Varna, Bulgaria. 1. Introduction.

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A New Operating Tool for Coding in Lossless Image Compression

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  1. A New Operating Tool for Coding in Lossless Image Compression Radu Rădescu University POLITEHNICA of Bucharest, Faculty of Electronics, Telecommunications and Information Technology Department of Applied Electronics and Information Engineering OC 2009, Varna, Bulgaria

  2. 1. Introduction • The application carries out the encoding for images with at most as 255 colors. • As source, the program accepts BMP files, represented on 8, 16 or 32 bits. • The application has an algorithm to reduce the color depth to 255 colors. • As output, the application produces two file types, named after the encoding type used to make them: • LZW, • RLC.

  3. 2. Implementing the compression (a) The algorithm: • The colors are stored in a table, which is built as the image is run through pixel by pixel. • If the color of the pixel exists in the table, it is ignored. • If not, it is added to the table. • Data storage is used, so that the search time for the color table can be substantially reduced.

  4. 2. Implementing the compression (b) The encoding flexibility: • In the case of LZW, a variable size dictionary is used, with 2048, 4096 or 8192 positions. • In the case of RLC, there are two ways to run through the image: up and down or left to right, which exploits in different ways the image correlation.

  5. 2. Implementing the compression (c) Reducing the number of colors: • The algorithm is based on the nearest color method, computed based on the mean square algorithm. • The generated color palette is a joint one, including 128 standard colors, allocated equally in the color space. • Other 127 colors are calculated based on the bar graph of the image.

  6. 3. Experimental results (a) The testing files: • A study was made on medical images with different degrees of correlation and different number of colors. • Two groups of different-size images were chosen: one with a large color dispersion, other with large areas of the same color. • Their number of colors was reduces gradually from 255 to 128 and, finally, to 16 colors.

  7. 3. Experimental results (b) NMR 600×600 pixels

  8. 3. Experimental results (c) Angiographies 600×600 pixels

  9. 3. Experimental results (d) Tomographies 600×600 pixels

  10. 3. Experimental results (e) Ultrasounds 540×405 pixels

  11. 3. Experimental results (f) X rays 540×405 pixels

  12. 3. Experimental results (g) • The LZW and RLC image files are compared to the original BMP format and to the archived RAR files. • The compression ratio is superior to GIF because of a better saving of coded words in files and of the built color palette obtained by the exact colors in the image. • The application has an advantage before the GIF compression as the file size is growing and the color number is decreasing.

  13. 3. Experimental results (h) The performance of coding for 255 colors

  14. 3. Experimental results (i) The performance of coding for 128 colors

  15. 4. Conclusions • The result does not depend much on the number of colors from the input image, but on the size of the source file. • The RLC compression depends more on the correlation within the image. • In the case of the 16 color images, the RLE compression for the BMP format (with the combination of two pixels on the same byte) is superior to the RLC format encoding. • Exceptions are images that, after decreasing the number of colors, have become more correlated, in advantage of the RLC encoding.

  16. Thank you!

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