New feature-based image adaptive vector-quantization coder

Jamshid Shanbehzadeh; Philip O. Ogunbona

doi:10.1117/12.228226

8 December 1995 New feature-based image adaptive vector-quantization coder

Jamshid Shanbehzadeh, Philip O. Ogunbona

Proceedings Volume 2605, Coding and Signal Processing for Information Storage; (1995) https://doi.org/10.1117/12.228226
Event: Photonics East '95, 1995, Philadelphia, PA, United States

Abstract

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.

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Jamshid Shanbehzadeh and Philip O. Ogunbona "New feature-based image adaptive vector-quantization coder", Proc. SPIE 2605, Coding and Signal Processing for Information Storage, (8 December 1995); https://doi.org/10.1117/12.228226

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KEYWORDS

Visualization

Image compression

Image quality

Quantization

Distortion

Information visualization

Visual compression

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