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.
ABSTRACT
In this paper we develop two entropy-coded subband image coding schemes. The difference between
these schemes is the procedure used for encoding the lowest frequency subband: predictive coding is
used in one system and transform coding in the other. Other subbands are encoded using zero-memory
quantization. After a careful study of subband statistics, the quantization parameters, the corresponding
Huffman codes and the bit allocation among subbands are all optimized. It is shown that both schemes
perform considerably better than the scheme developed by Woods and O'Neil [2]. Roughly speaking,
these new schemes perform the same as that in [2] at half the encoding rate. To make a complete
comparison against the results in [2] , we have studied the performance of the two schemes developed here
as well as that of [2] in the presence of channel noise. After developing a codeword packetization scheme,
we demonstrate that the scheme in [2] exhibits significantly higher robustness against the transmission
noise.
Conference Committee Involvement (2)
Visual Communications and Image Processing 2004
20 January 2004 | San Jose, California, United States
Image and Video Communications and Processing 2003
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