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
벡터 양자화(VQ)는 매력적인 이미지 압축 기술입니다. VQ는 블록 내 이웃 픽셀 간의 높은 상관 관계를 활용하지만 인접 블록 간의 높은 상관 관계는 무시합니다. VQ와 달리 SMVQ(Side-Match VQ)는 인코딩된 두 인접 블록(상단 및 왼쪽 블록)의 코드워드 정보를 활용하여 현재 입력 벡터를 인코딩합니다. 그러나 SMVQ는 고정 비트 전송률 압축 기술이며 입력 벡터를 예측하기 위해 에지 특성을 최대한 활용하지 않습니다. CSMVQ(Classified Side-Match Vector Quantization)는 비트 전송률이 낮고 재구성 품질이 상대적으로 높은 효과적인 이미지 압축 기술입니다. 이웃 블록의 코드워드의 변화를 사용하여 입력 벡터가 어떤 클래스에 속하는지 결정하기 위해 블록 분류기를 활용합니다. 이에 대한 대안으로 본 논문에서는 주변 블록의 코드워드의 기울기 값을 이용하여 입력 블록을 예측하는 XNUMX가지 알고리즘을 제안한다. 첫 번째는 CSMVQ와 유사한 기본 그래디언트 기반 분류기를 사용합니다. 더 낮은 비트 전송률을 달성하기 위해 두 번째는 개선된 XNUMX단계 분류기 구조를 활용합니다. 인코딩 시간을 더욱 줄이기 위해 마지막 분류기는 다양한 예측 결과에 따라 그래디언트 순서의 마스터 코드북 내에서 적응형 클래스 코드북을 정의하는 보다 효율적인 분류기를 사용합니다. 실험 결과는 제안된 알고리즘의 효율성을 입증했다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -