Using vector quantization for image processing

A review is presented of vector quantization, the mapping of pixel intensity vectors into binary vectors indexing a limited number of possible reproductions, which is a popular image compression algorithm. Compression has traditionally been done with little regard for image processing operations that may precede or follow the compression step. Recent work has used vector quantization both to simplify image processing tasks, such as enhancement classification, halftoning, and edge detection, and to reduce the computational complexity by performing the tasks simultaneously with the compression. The fundamental ideas of vector quantization are explained, and vector quantization algorithms that perform image processing are surveyed     

Variable rate vector quantization for speech, image, and videocompression

The performance of a vector quantizer can be improved by using a variable-rate code. Three variable-rate vector quantization systems are applied to speech, image, and video sources and compared to standard vector quantization and noiseless variable-rate coding approaches. The systems range from a simple and flexible tree-based vector quantizer to a high-performance, but complex, jointly optimized vector quantizer and noiseless code. The systems provide significant performance improvements for subband speech coding, predictive image coding, and motion-compensated video, but provide only marginal improvements for vector quantization of linear predictive coefficients in speech and direct vector quantization of images. Criteria are suggested for determining when variable-rate vector quantization may provide significant performance improvement over standard approaches     

Index assignment for progressive transmission of full-search vectorquantization

The authors study codeword index assignment to allow for progressive image transmission of fixed rate full-search vector quantization (VQ). They develop three new methods of assigning indices to a vector quantization codebook and formulate these assignments as labels of nodes of a full-search progressive transmission tree. The tree is used to design intermediate codewords for the decoder so that full-search VQ has a successive approximation character. The binary representation for the path through the tree represents the progressive transmission code. The methods of designing the tree that they apply are the generalized Lloyd algorithm, minimum cost perfect matching from optimization theory, and a method of principal component partitioning. Their empirical results show that the final method gives intermediate signal-to-noise ratios (SNRs) that are close to those obtained with tree-structured vector quantization, yet they have higher final SNRs.