Hybrid Image Coding Based On Local-Variant Source Models

A hybrid coding method based on estimation and detection of local features and classification of image data is presented in this paper. The local edge-orientations as well as the statistical properties are detected and estimated prior to the vector and the scalar quantization of the DCT coefficients. In a specific "feature prediction" process, both the local average grey-level which determines the DC coefficient and the local variance which contributes the ac energy distribution are estimated for each 8x8 image block by using the surrounding pixels of the block. In this way, the nonstationary image data are locally classified into sub-sources with each sub-source containing its own specific characteristics. The generally existent statistical dependences between the neighboring transform blocks are also exploited due to the feature prediction operation. The classification information which contains the local edge orientation and the local variance, determines the ac-energy distribution and consequently the vector forming of the ac coefficients in the vector quantization process. An adaptive scalar quantizer which is controlled by both the classification information and the channel buffer is then followed and clearly, the properties of the human visual system can be incorporated with this supplementary scalar quantization process to improve the coding performance. At a bit-rate round 1 bit/pixel, very good image quality can be observed with high signal-noise-ratio (up to 40dB).