Applications of gain-spectral-block classification in image coding

Hamid Jafarkhani; M. Kerry; Nariman Farvardin

doi:10.1117/12.204119

3 March 1995 Applications of gain-spectral-block classification in image coding

Hamid Jafarkhani, M. Kerry, Nariman Farvardin

Proceedings Volume 2418, Still-Image Compression; (1995) https://doi.org/10.1117/12.204119
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

This work focuses on the development of a two-level image block classification scheme and its application to low bit rate image coding. Using this classifier, we present two adaptive encoding structures, one based on vector quantization (VQ) and the other based on transform coding. The first stage of our system classifies the image blocks into K₁ classes based on the block grain, similar to the well-known classification scheme of Chen and Smith, but allows for the possibility of a variable number of vectors per class. To do this, we develop an iterative mini-max algorithm that adjusts the vectors among the classes so that the resulting mean-normalized standard deviation of the gain values within any class is similar to all other classes. After classifying based on block gain values, we further classify each gain-class into K₂ spectral classes. This is accomplished by performing a 1D LPC-type analysis of each block, and clustering the resulting LPC vectors using a vector quantizer (VQ) with K₂ codevectors. In order to make this spectral matching meaningful, the VQ is designed and implemented using the Itakura-Saito distortion measure. The resulting two-level classification scheme thus classifies an image into K equals K₁K₂ classes. A system consisting of a bank of K fixed-rate Multi-Stage VQ's and a DCT based system are then used to examine the usefulness of the proposed approaches for classification.

Citation Download Citation

Hamid Jafarkhani, M. Kerry, and Nariman Farvardin "Applications of gain-spectral-block classification in image coding", Proc. SPIE 2418, Still-Image Compression, (3 March 1995); https://doi.org/10.1117/12.204119

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