Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83520
Title: Just-Noticeable Difference-Based Perceptual Optimization for JPEG Compression
Authors: Zhang, Xinfeng
Wang, Shiqi
Gu, Ke
Lin, Weisi
Ma, Siwei
Gao, Wen
Keywords: Just-noticeable difference (JND)
Image compression
Issue Date: 2016
Source: Zhang, X., Wang, S., Gu, K., Lin, W., Ma, S., & Gao, W. (2017). Just-Noticeable Difference-Based Perceptual Optimization for JPEG Compression. IEEE Signal Processing Letters, 24(1), 96-100.
Series/Report no.: IEEE Signal Processing Letters
Abstract: The Quantization table in JPEG, which specifies the quantization scale for each discrete cosine transform (DCT) coefficient, plays an important role in image codec optimization. However, the generic quantization table design that is based on the characteristics of human visual system (HVS) cannot adapt to the variations of image content. In this letter, we propose a just-noticeable difference (JND) based quantization table derivation method for JPEG by optimizing the rate-distortion costs for all the frequency bands. To achieve better perceptual quality, the DCT domain JND-based distortion metric is utilized to model the stair distortion perceived by HVS. The rate-distortion cost for each band is derived by estimating the rate with the first-order entropy of quantized coefficients. Subsequently, the optimal quantization table is obtained by minimizing the total rate-distortion costs of all the bands. Extensive experimental results show that the quantization table generated by the proposed method achieves significant bit-rate savings compared with JPEG recommended quantization table and specifically developed quantization tables in terms of both objective and subjective evaluations.
URI: https://hdl.handle.net/10356/83520
http://hdl.handle.net/10220/42621
ISSN: 1070-9908
DOI: 10.1109/LSP.2016.2641456
Schools: School of Computer Science and Engineering 
School of Electrical and Electronic Engineering 
Research Centres: Rapid-Rich Object Search Lab 
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2016.2641456].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
SCSE Journal Articles

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