D.G. Sampson

A successive approximation vector quantizer for wavelet transform image coding

A coding method for wavelet coefficients of images using vector quantization, called successive approximation vector quantization (SA-W-VQ) is proposed. In this method, each vector is coded by a series of vectors of decreasing magnitudes until a certain distortion level is reached. The successive approximation using vectors is analyzed, and conditions for convergence are derived. It is shown that lattice codebooks are an efficient tool for meeting these conditions without the need for very large codebooks. Regular lattices offer the extra advantage of fast encoding algorithms. In SA-W-VQ, distortion equalization of the wavelet coefficients can be achieved together with high compression ratio and precise bit-rate control. The performance of SA-W-VQ for still image coding is compared against some of the most successful image coding systems reported in the literature. The comparison shows that SA-W-VQ performs remarkably well at several bit rates and in various test images.

Super high definition image coding using wavelet vector quantization

A method of coding super high definition (SHD) still images based on vector quantization of wavelet coefficients is proposed. A compression scheme for SHD images should achieve data compression without any visible picture quality deterioration. We demonstrate that the proposed method meets this requirement. In this coding technique, each vector of wavelet coefficients is coded by a series of vectors of decreasing magnitudes, resulting in a successive approximation process. It also exploits the structural similarities among the bands. This provides efficient coding together with the ability to guarantee arbitrary distortion levels for each band, which can be exploited to achieve subjectively optimum performance. Conventional image compression techniques such as transform, sub-band, and vector quantization have already been tested for the coding of SHD images. Simulation results show that the proposed method outperforms the other SHD image coding methods reported in the literature.

Image coding using successive approximation wavelet vector quantization

A novel coding method of wavelet coefficients of images using vector quantization, referred to as successive approximation wavelet vector quantization (SA-W-VQ) is proposed. In this method, each vector is coded by a series of vectors of decreasing magnitudes until a certain distortion level is reached. Analysis of the successive approximation using vectors is given, and conditions for convergence are derived. It is shown that lattice codebooks offer an efficient tool to meet these conditions, with the extra advantage of fast encoding algorithms. In SA-W-VQ, distortion equalization of the wavelet coefficients can be achieved together with high compression ratio and precise bit rate control. Simulation results for still image coding show that SA-W-VQ outperforms both the EZW coder and the standard JPEG.

Wavelet lattice quantization for low bit rate video coding

A method for low bit rate video coding based on successive approximation wavelet vector quantisation and overlapped block matching motion compensation is described. The main advantage of this scheme is that the most important data of the motion compensated interframe prediction error image are coded with priority. Simulation results are given to evaluate the performance of the coding system at 64 kbit/sec.

Image and video compression for multimedia applications

Publisher Summary This chapter discusses a coding method that is suitable for multimedia applications. This method is based on the efficient coding of image wavelet coefficients using zerotree multi-stage lattice vector quantization (VQ). This method is referred as successive approximation wavelet VQ (SA-W-VQ). The basic idea in SA-W-VQ is that the original blocks of wavelet coefficients are successively refined based on vectors of progressively decreasing magnitude and a finite set of prototype orientations. Block zero-tree prediction and adaptive arithmetic coding are incorporated to improve the efficiency of the codec. The chapter explains that this coding scheme achieves high compression ratios with good picture quality, maintaining a very simple implementation. Simulation results are provided to evaluate the coding performance of the described coding scheme for still image and low bit rate video coding. Comparison with both the standard JPEG coder and the RM8 implementation of the standard H.261 video codec shows that the presented codec provides improvements in both the peak signal-to-noise ratio and the picture quality.

Super high definition image coding using successive approximation wavelet vector quantization

A method of coding super high definition (SHD) still images based on vector quantization of wavelet coefficients is proposed. In this coding technique, each block of wavelet coefficients is coded by a series of vectors of decreasing magnitudes, resulting in a successive approximation process. It also exploits the structural similarities among the bands. This provides efficient coding together with the ability to guarantee arbitrary distortion levels for each band, which can be exploited to achieve optimum bit allocation. Since SHD images must have high quality, they must be compressed without any visible picture quality deterioration. Due to the huge amount of image data involved, simplicity of the coding system is important to maintain acceptable processing times. Also, a hierarchical structure is desirable to provide compatibility with other existing lower resolution formats, like in multi-media applications. We demonstrate that the proposed method meets these requirements. Conventional image compression techniques such as transform, subband and vector quantization have already been tested for the coding of SHD images. Simulation results show that the proposed method achieves excellent coding performance and it outperforms other SHD image coding methods reported so far by more than 7 dB.