Compression of color facial images using feature correction two-stage vector quantization

Jincheng Huang; Yao Wang

doi:10.1109/83.736696

Compression of color facial images using feature correction two-stage vector quantization

Jincheng Huang, Yao Wang

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The RGB-FC2VQ algorithm is found to yield better image quality than KLT-FC2VQ or YCbCr-FC2VQ at similar bit rates. With the RGB-FC2VQ algorithm, a 128 × 128 24-b color ID image (49 152 bytes) can be compressed down to about 500 bytes with satisfactory quality. When the codeword indices are further compressed losslessy using a first order Huffman coder, this size is further reduced to about 450 bytes.

Original language	English (US)
Pages (from-to)	102-109
Number of pages	8
Journal	IEEE Transactions on Image Processing
Volume	8
Issue number	1
DOIs	https://doi.org/10.1109/83.736696
State	Published - 1999

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

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title = "Compression of color facial images using feature correction two-stage vector quantization",

abstract = "A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The RGB-FC2VQ algorithm is found to yield better image quality than KLT-FC2VQ or YCbCr-FC2VQ at similar bit rates. With the RGB-FC2VQ algorithm, a 128 × 128 24-b color ID image (49 152 bytes) can be compressed down to about 500 bytes with satisfactory quality. When the codeword indices are further compressed losslessy using a first order Huffman coder, this size is further reduced to about 450 bytes.",

author = "Jincheng Huang and Yao Wang",

note = "Funding Information: Manuscript received September 16, 1996; revised March 19, 1998. This work was supported in part by Symbol Technologies, Inc., Holtsville, NY. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Michael W. Marcellin. The authors are with the Department of Electrical Engineering, Polytechnic University, Brooklyn, NY 11201 USA (e-mail: yao@vision.poly.edu). Publisher Item Identifier S 1057-7149(99)00223-7.",

year = "1999",

doi = "10.1109/83.736696",

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N1 - Funding Information: Manuscript received September 16, 1996; revised March 19, 1998. This work was supported in part by Symbol Technologies, Inc., Holtsville, NY. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Michael W. Marcellin. The authors are with the Department of Electrical Engineering, Polytechnic University, Brooklyn, NY 11201 USA (e-mail: yao@vision.poly.edu). Publisher Item Identifier S 1057-7149(99)00223-7.

PY - 1999

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N2 - A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The RGB-FC2VQ algorithm is found to yield better image quality than KLT-FC2VQ or YCbCr-FC2VQ at similar bit rates. With the RGB-FC2VQ algorithm, a 128 × 128 24-b color ID image (49 152 bytes) can be compressed down to about 500 bytes with satisfactory quality. When the codeword indices are further compressed losslessy using a first order Huffman coder, this size is further reduced to about 450 bytes.

AB - A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The RGB-FC2VQ algorithm is found to yield better image quality than KLT-FC2VQ or YCbCr-FC2VQ at similar bit rates. With the RGB-FC2VQ algorithm, a 128 × 128 24-b color ID image (49 152 bytes) can be compressed down to about 500 bytes with satisfactory quality. When the codeword indices are further compressed losslessy using a first order Huffman coder, this size is further reduced to about 450 bytes.

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Compression of color facial images using feature correction two-stage vector quantization

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