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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kuantiti vektor (VQ) ialah teknik pemampatan imej yang menarik. VQ menggunakan korelasi tinggi antara piksel bersebelahan dalam blok, tetapi mengabaikan korelasi tinggi antara blok bersebelahan. Tidak seperti VQ, padanan sisi VQ (SMVQ) mengeksploitasi maklumat kata kod dua blok bersebelahan yang dikodkan, blok atas dan kiri, untuk mengekod vektor input semasa. Walau bagaimanapun, SMVQ ialah teknik pemampatan kadar bit tetap dan tidak menggunakan sepenuhnya ciri tepi untuk meramalkan vektor input. Kuantiti vektor padanan sisi terkelas (CSMVQ) ialah teknik pemampatan imej yang berkesan dengan kadar bit yang rendah dan kualiti pembinaan semula yang agak tinggi. Ia mengeksploitasi pengelas blok untuk menentukan kelas mana yang dimiliki oleh vektor input menggunakan varians kata kod blok jiran. Sebagai alternatif, kertas kerja ini mencadangkan tiga algoritma menggunakan nilai kecerunan kata kod blok jiran untuk meramalkan blok input. Yang pertama menggunakan pengelas berasaskan kecerunan asas yang serupa dengan CSMVQ. Untuk mencapai kadar bit yang lebih rendah, yang kedua mengeksploitasi struktur pengelas dua peringkat yang diperhalusi. Untuk mengurangkan lagi masa pengekodan, yang terakhir menggunakan pengelas yang lebih cekap, di mana buku kod kelas adaptif ditakrifkan dalam buku kod induk tertib kecerunan mengikut pelbagai hasil ramalan. Keputusan eksperimen membuktikan keberkesanan algoritma yang dicadangkan.
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Salinan
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
Salinan
@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},}
Salinan
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 -