Combining nonlinear multiresolution system and vector quantization for still image compression

It is popular to use multiresolution systems for image coding and compression. However, general-purpose techniques such as filter banks and wavelets are linear. While these systems are rigorous, nonlinear features in the signals cannot be utilized in a single entity for compression. Linear filters are known to blur the edges. Thus, the low-resolution images are typically blurred, carrying little information. We propose and demonstrate that edge- preserving filters such as median filters can be used in generating a multiresolution system using the Laplacian pyramid. The signals in the detail images are small and localized in the edge areas. Principal component vector quantization (PCVQ) is used to encode the detail images. PCVQ is a tree-structured VQ which allows fast codebook design and encoding/decoding. In encoding, the quantization error at each level is fed back through the pyramid to the previous level so that ultimately all the error is confined to the first level. With simple coding methods, we demonstrate that images with PSNR 33 dB can be obtained at 0.66 bpp without the use of entropy coding. When the rate is decreased to 0.25 bpp, the PSNR of 30 dB can still be achieved. Combined with an earlier result, our work demonstrate that nonlinear filters can be used for multiresolution systems and image coding

Hadamard transform based Equal-average Equal-variance Equal-norm nearest neighbor codeword search algorithm

New York, US

Finite state lattice vector quantization for wavelet-based image coding

IEEE International Symposium on Circuits and Systems, Hong Kong, China, 9-12 June 1997It is well known that there exists strong energy correlation between various subbands of a real-world image. A new powerful technique of Finite State Vector Quantization (FSVQ) has been introduced to fully exploit the self-similarity of the image in wavelet domain across different scales. Lattices in RN have considerable structure, and hence, Lattice VQ offers the promise of design simplicity and reduced complexity encoding. The combination of FSVQ and LVQ gives rise to the so-called FSLVQ, which is proved to be successful in exploiting the energy correlation across scales and simple enough in implementation.published_or_final_versio

Fast search algorithms for ECVQ using projection pyramids and variance of codewords

金沢大学大学院自然科学研究科情報システム金沢大学工学部Vector quantization for image compression requires expensive time to find the closest codeword through the codebook. Codebook design based on empirical data for entropy-constrained vector quantization (ECVQ) involves a time consuming training phase in which a Lagrangian cost measure has to be minimized over the set of codebook vectors. In this paper, we propose two fast codebook generation methods for ECVQ. In the first one, we use an appropriate topological structure of input vectors and codewords to reject many codewords that are impossible to be candidates for the best codeword. In the second method, we use the variance test to increase the ability of the first algorithm to reject more codewords. These algorithms allow significant acceleration in the codebook design process. Experimental results are presented on image block data. These results show that our new algorithms perform better than the previously known methods