DocumentCode :
1055767
Title :
DCRVQ: a new strategy for efficient entropy coding of vector-quantized images
Author :
De Natale, Francesco G B ; Fioravanti, Stefano ; Giusto, Daniele D.
Author_Institution :
Dept. of Biophys. & Electr. Eng., Genova Univ., Italy
Volume :
44
Issue :
6
fYear :
1996
fDate :
6/1/1996 12:00:00 AM
Firstpage :
696
Lastpage :
706
Abstract :
This paper presents a novel predictive coding scheme for image-data compression by vector quantization (VQ). On the basis of a prediction, further compression is achieved by using a dynamic codebook-reordering strategy that allows a more efficient Huffman encoding of vector addresses. The proposed method is lossless, for it increases the compression performances of a baseline vector quantization scheme, without causing any further image degradation. Results are presented and a comparison with Cache-VQ is made
Keywords :
Huffman codes; entropy codes; image coding; prediction theory; vector quantisation; Cache-VQ; DCRVQ; Huffman encoding; compression performance; dynamic codebook reordering VQ; entropy coding; image data compression; lossless coding method; neural approach; predictive coding; vector addresses; vector quantized images; Data compression; Degradation; Encoding; Entropy coding; Image coding; Mean square error methods; Multidimensional systems; Pixel; Predictive coding; Vector quantization;
fLanguage :
English
Journal_Title :
Communications, IEEE Transactions on
Publisher :
ieee
ISSN :
0090-6778
Type :
jour
DOI :
10.1109/26.506386
Filename :
506386
Link To Document :
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