University of Wollongong
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New feature-based image adaptive vector quantisation coder

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conference contribution
posted on 2024-11-14, 08:55 authored by Jamshid Shanbehzadeh, Philip OgunbonaPhilip Ogunbona
It is difficult to achieve a good low bit rate image compression performance with traditional block coding schemes such as transform coding and vector quantization, without regard for the human visual perception or signal dependency. These classical block coding schemes are based on minimizing the MSE at a certain rate. This procedure results in more bits being allocated to areas which may not be visually important and the resulting quantization noise manifests as a blocking artifact. Blocking artifacts are known to be psychologically more annoying than white noise when the human visual response is considered. While image adaptive vector quantization (IAVQ) attempts to address this problem for traditional vector quantization (VQ) schemes by exploiting image dependency, it ignores the human visual perception when allocating bits. This paper addresses this problem through a new IAVQ scheme based on the human visual perception. In this method, the input image is partitioned into visual classes and each class, depending on its visual importance, is adaptively or universally encoded. The objective and subjective quality of this scheme has been compared with JPEG and a previously proposed image adaptive VQ scheme. The new scheme subjectively outperforms both schemes at low bit rates.

History

Citation

Shanbehzadeh, J. & Ogunbona, P. O. (1995). New feature-based image adaptive vector quantisation coder. Proceedings of SPIE - Coding and Signal Processing for Information Storage (pp. 170-181). The International Society for Optical Engineering.

Parent title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

2605

Pagination

170-181

Language

English

RIS ID

65871

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