The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A quantização vetorial (VQ) é uma técnica atraente de compressão de imagem. VQ utiliza a alta correlação entre pixels vizinhos em um bloco, mas desconsidera a alta correlação entre os blocos adjacentes. Ao contrário do VQ, o VQ de correspondência lateral (SMVQ) explora informações de palavras de código de dois blocos adjacentes codificados, os blocos superior e esquerdo, para codificar o vetor de entrada atual. No entanto, SMVQ é uma técnica de compressão de taxa de bits fixa e não faz uso total das características da borda para prever o vetor de entrada. A quantização vetorial de correspondência lateral classificada (CSMVQ) é uma técnica eficaz de compressão de imagem com baixa taxa de bits e qualidade de reconstrução relativamente alta. Ele explora um classificador de blocos para decidir a qual classe o vetor de entrada pertence usando as variações das palavras-código dos blocos vizinhos. Como alternativa, este artigo propõe três algoritmos que utilizam valores de gradiente de palavras-código de blocos vizinhos para prever o bloco de entrada. O primeiro emprega um classificador básico baseado em gradiente semelhante ao CSMVQ. Para obter taxas de bits mais baixas, o segundo explora uma estrutura refinada de classificador de dois níveis. Para reduzir ainda mais o tempo de codificação, o último emprega um classificador mais eficiente, no qual livros de códigos de classe adaptativos são definidos dentro de um livro de códigos mestre ordenado por gradiente de acordo com vários resultados de previsão. Resultados experimentais comprovam a eficácia dos algoritmos propostos.
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Zhe-Ming LU, Bian YANG, Sheng-He SUN, "Image Compression Algorithms Based on Side-Match Vector Quantizer with Gradient-Based Classifiers" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 9, pp. 1409-1415, September 2002, doi: .
Abstract: Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_9_1409/_p
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@ARTICLE{e85-d_9_1409,
author={Zhe-Ming LU, Bian YANG, Sheng-He SUN, },
journal={IEICE TRANSACTIONS on Information},
title={Image Compression Algorithms Based on Side-Match Vector Quantizer with Gradient-Based Classifiers},
year={2002},
volume={E85-D},
number={9},
pages={1409-1415},
abstract={Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Image Compression Algorithms Based on Side-Match Vector Quantizer with Gradient-Based Classifiers
T2 - IEICE TRANSACTIONS on Information
SP - 1409
EP - 1415
AU - Zhe-Ming LU
AU - Bian YANG
AU - Sheng-He SUN
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2002
AB - Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.
ER -