DocumentCode :
2617214
Title :
Compression of vector quantization code sequences based on code frequencies and spatial redundancies
Author :
Kangas, Jari ; Kaski, Samuel
Author_Institution :
Neural Networks Res. Centre, Helsinki Univ. of Technol., Espoo, Finland
Volume :
3
fYear :
1996
fDate :
16-19 Sep 1996
Firstpage :
463
Abstract :
Vector quantization (VQ) can be used to compress images with high compression ratios. The VQ methods produce a sequence of code values which identifies the codebook model vectors to be used as blocks of pixels in the decoded image. In this paper we define a novel non-lossy and computationally efficient method to further compress the code sequence based on the relative frequencies of the code values, and the spatial distribution of each code. In an example case we reduced the bit rate by 29%. A further reduction of 7 percentage units was obtained when the VQ codebook was produced by the self-organizing map (SOM) algorithm. A SOM codebook has the property that similar blocks have similar codes, which was used to take advantage of spatial redundancies in the image
Keywords :
image coding; redundancy; self-organising feature maps; sequential codes; vector quantisation; VQ codebook; code frequencies; code sequence compression; codebook model vectors; computationally efficient method; image compression; self-organizing map algorithm; spatial distribution; spatial redundancies; vector quantization code sequences; Bit rate; Decoding; Electronic mail; Entropy; Frequency; Gray-scale; Image coding; Neural networks; Pixel; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1996. Proceedings., International Conference on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3259-8
Type :
conf
DOI :
10.1109/ICIP.1996.560531
Filename :
560531
Link To Document :
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