A Bit Rate Reduction Technique for Vector Quantization Image Data Compression

Yung-Gi WU; Shen-Chuan TAI

A Bit Rate Reduction Technique for Vector Quantization Image Data Compression

Yung-Gi WU, Shen-Chuan TAI

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In this paper, a technique to reduce the overhead of Vector Quantization (VQ) coding is developed here. Our method exploits the inter-index correlation property to reduce the overhead to transmit encoded indices. Discrete Cosine Transform (DCT) is the tool to decorrelate the above correlation to get further bit rate reduction. As we know, the codewords in the codebook that generated from conventional LBG algorithm do not have any specified orders. Hence, the indices for selected codewords to represent respective adjacent blocks are random distributions. However, due to the homogeneous property existing among adjacent regions in original image, we re-arrange the codebook according to our predefined weighting criterion to enable the selected neighboring indices capable of indicating the homogeneous feature as well. Then, DCT is used to compress those VQ encoded indices. Because of the homogeneous characteristics existing among the selected adjacent indices after codebook permutation, DCT can achieve better compression efficiency. However, as we know, DCT introduces distortion by the quantization procedure, which yield error-decoded indices. Therefore, we utilize an index residue compensation method to make up that error decoded indices which have high complexity deviation to reduce those unpleasant visual effects caused by distorted indices. Statistics illustrators and table are addressed to demonstrate the efficient performance of proposed method. Experiments are carried out to Lena and other natural gray images to demonstrate our claims. Simulation results show that our method saves more than 50% bit rate to some images while preserving the same reconstructed image qualities as standard VQ coding scheme.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E82-A No.10 pp.2147-2153

Publication Date: 1999/10/25

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Type of Manuscript: Special Section PAPER (Special Section on Information Theory and Its Applications)

Category: Source Coding/Image Processing

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Yung-Gi WU, Shen-Chuan TAI, "A Bit Rate Reduction Technique for Vector Quantization Image Data Compression" in IEICE TRANSACTIONS on Fundamentals, vol. E82-A, no. 10, pp. 2147-2153, October 1999, doi: .
Abstract: In this paper, a technique to reduce the overhead of Vector Quantization (VQ) coding is developed here. Our method exploits the inter-index correlation property to reduce the overhead to transmit encoded indices. Discrete Cosine Transform (DCT) is the tool to decorrelate the above correlation to get further bit rate reduction. As we know, the codewords in the codebook that generated from conventional LBG algorithm do not have any specified orders. Hence, the indices for selected codewords to represent respective adjacent blocks are random distributions. However, due to the homogeneous property existing among adjacent regions in original image, we re-arrange the codebook according to our predefined weighting criterion to enable the selected neighboring indices capable of indicating the homogeneous feature as well. Then, DCT is used to compress those VQ encoded indices. Because of the homogeneous characteristics existing among the selected adjacent indices after codebook permutation, DCT can achieve better compression efficiency. However, as we know, DCT introduces distortion by the quantization procedure, which yield error-decoded indices. Therefore, we utilize an index residue compensation method to make up that error decoded indices which have high complexity deviation to reduce those unpleasant visual effects caused by distorted indices. Statistics illustrators and table are addressed to demonstrate the efficient performance of proposed method. Experiments are carried out to Lena and other natural gray images to demonstrate our claims. Simulation results show that our method saves more than 50% bit rate to some images while preserving the same reconstructed image qualities as standard VQ coding scheme.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_10_2147/_p

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@ARTICLE{e82-a_10_2147,
author={Yung-Gi WU, Shen-Chuan TAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Bit Rate Reduction Technique for Vector Quantization Image Data Compression},
year={1999},
volume={E82-A},
number={10},
pages={2147-2153},
abstract={In this paper, a technique to reduce the overhead of Vector Quantization (VQ) coding is developed here. Our method exploits the inter-index correlation property to reduce the overhead to transmit encoded indices. Discrete Cosine Transform (DCT) is the tool to decorrelate the above correlation to get further bit rate reduction. As we know, the codewords in the codebook that generated from conventional LBG algorithm do not have any specified orders. Hence, the indices for selected codewords to represent respective adjacent blocks are random distributions. However, due to the homogeneous property existing among adjacent regions in original image, we re-arrange the codebook according to our predefined weighting criterion to enable the selected neighboring indices capable of indicating the homogeneous feature as well. Then, DCT is used to compress those VQ encoded indices. Because of the homogeneous characteristics existing among the selected adjacent indices after codebook permutation, DCT can achieve better compression efficiency. However, as we know, DCT introduces distortion by the quantization procedure, which yield error-decoded indices. Therefore, we utilize an index residue compensation method to make up that error decoded indices which have high complexity deviation to reduce those unpleasant visual effects caused by distorted indices. Statistics illustrators and table are addressed to demonstrate the efficient performance of proposed method. Experiments are carried out to Lena and other natural gray images to demonstrate our claims. Simulation results show that our method saves more than 50% bit rate to some images while preserving the same reconstructed image qualities as standard VQ coding scheme.},
keywords={},
doi={},
ISSN={},
month={October},}

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TY - JOUR
TI - A Bit Rate Reduction Technique for Vector Quantization Image Data Compression
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2147
EP - 2153
AU - Yung-Gi WU
AU - Shen-Chuan TAI
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1999
AB - In this paper, a technique to reduce the overhead of Vector Quantization (VQ) coding is developed here. Our method exploits the inter-index correlation property to reduce the overhead to transmit encoded indices. Discrete Cosine Transform (DCT) is the tool to decorrelate the above correlation to get further bit rate reduction. As we know, the codewords in the codebook that generated from conventional LBG algorithm do not have any specified orders. Hence, the indices for selected codewords to represent respective adjacent blocks are random distributions. However, due to the homogeneous property existing among adjacent regions in original image, we re-arrange the codebook according to our predefined weighting criterion to enable the selected neighboring indices capable of indicating the homogeneous feature as well. Then, DCT is used to compress those VQ encoded indices. Because of the homogeneous characteristics existing among the selected adjacent indices after codebook permutation, DCT can achieve better compression efficiency. However, as we know, DCT introduces distortion by the quantization procedure, which yield error-decoded indices. Therefore, we utilize an index residue compensation method to make up that error decoded indices which have high complexity deviation to reduce those unpleasant visual effects caused by distorted indices. Statistics illustrators and table are addressed to demonstrate the efficient performance of proposed method. Experiments are carried out to Lena and other natural gray images to demonstrate our claims. Simulation results show that our method saves more than 50% bit rate to some images while preserving the same reconstructed image qualities as standard VQ coding scheme.
ER -