A New Operating Tool for Coding in Lossless Image Compression Radu Rădescu University POLITEHNICA of Bucharest, Faculty of Electronics, Telecommunications.

A New Operating Tool for A New Operating Tool for Coding Coding

in Lossless Image in Lossless Image CompressionCompression

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. Introduction1. 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.

2. 2. Implementing the Implementing the compression (a)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.

2. 2. Implementing the Implementing the compression (b)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 orleft to right,

which exploits in different ways the image correlation.

2. 2. Implementing the Implementing the compression (c)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.

3. 3. Experimental results (a)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.

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

3. 3. Experimental results (c)Experimental results (c)

Angiographies 600×600 pixels

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

3. 3. Experimental results (e)Experimental results (e)

Ultrasounds 540×405 pixels

3. 3. Experimental results (f)Experimental results (f)

X rays 540×405 pixels

3. 3. Experimental results Experimental results (g)(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.

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

3. 3. Experimental results (i)Experimental results (i)

The performance of coding for 128 colors

44. Conclusions. 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.

Thank you!Thank you!