Tree-searched Vector Quantization Research Articles

A new efficient image compression technique with index-matching vector quantization

A new efficient image compression technique is presented for low cost applications, such as multimedia and video-conferencing. Since address vector quantization (A-VQ) proposed by Nasrabadi and Feng (1990) for image coding, has the main disadvantage of high computational complexity of reordering the address codebook at the transmitter and the receiver during the encoding of each block, we propose a new efficient approach to overcome this disadvantage. The proposed algorithm is based on tree search vector quantization via, multi-path search and index matching in the index codebook, and may achieve a better performance as well as low computational complexity. We theoretically prove that the proposed algorithm is superior to the A-VQ algorithm and experimentally show that a lower bit rate than that of the A-VQ is obtained.

Finite state vector quantization with multipath tree search strategy for image/video coding

This paper presents a new vector quantization (VQ) algorithm exploiting the features of tree-search as well as finite state VQs for image/video coding. In the tree-search VQ, multiple candidates are identified for ongoing search to optimally determine an index of the minimum distortion. In addition, the desired codebook has been reorganized hierarchically to meet the concept of multipath search of neighboring trees so that picture quality can be improved by 4 dB on the average. In the finite state VQ, adaptation to the state codebooks is added to enhance the hit ratio of the index produced by the tree-search VQ. Thus, compressed bits can be further reduced. An identifier code is then included to indicate to which output indexes belong. Therefore, this modified algorithm not only reaches a higher compression ratio, but also achieves better quality compared to conventional finite-state and tree-search VQs. Finally, suitable VLSI architectures for real-time performance are proposed here (1) to remove the bottleneck of iteration bound in finite-state VQ and (2) to provide parallel computing structure for tree-search VQ to meet computational requirements.

Comparison between several adaptive search vector quantization schemes and JPEG standard for image compression

This paper introduces an adaptive search vector quantization (VQ) algorithm, hierarchical adaptive search VQ (HASVQ) and compares its performance against several previously proposed adaptive search image compression algorithms. These algorithms are: the JPEG standard, adaptive tree search vector quantization (ATSVQ), multi-stage adaptive search vector quantization (MASVQ). The bit-rate examined is from 0.625 to 1.25 bits per pixel (bpp). The results indicate that HASVQ outperforms all the other algorithms over the entire range of bit-rates examined. For higher bit-rates MASVQ performs next to the HASVQ algorithm and is, in-turn, followed by the JPEG standard. ATSVQ performs well at lower bit-rates, however its performance at higher bit-rates is suppressed by not having an adequate codebook size. >

VLSI systolic binary tree-searched vector quantizer for image compression

A high-speed image compression VLSI processor based on the systolic architecture of difference-codebook binary tree-searched vector quantization has been developed to meet the increasing demands on large-volume data communication and storage requirements. Simulation results show that this design is applicable to many types of image data and capable of producing good reconstructed data quality at high compression ratios. Various design aspects of the binary tree-searched vector quantizer including the algorithm, architecture, and detailed functional design are thoroughly investigated for VLSI implementation. An 8-level difference-codebook binary tree-searched vector quantizer can be implemented on a custom VLSI chip that includes a systolic array of eight identical processors and a hierarchical memory of eight subcodebook memory banks. The total transistor count is about 300000 and the die size is about 8.67/spl times/7.72 mm/sup 2/ in a 1.0 /spl mu/m CMOS technology. The throughput rate of this high-speed VLSI compression system is approximately 25 Mpixels per second and its equivalent computation power is 600 million instructions per second.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Vector quantization using tree-structured self-organizing feature maps

In this paper, we propose a binary-tree structure neural network model suitable for structured clustering. During and after training, the centroids of the clusters in this model always form a binary tree in the input pattern space. This model is used to design tree search vector quantization codebooks for image coding. Simulation results show that the acquired codebook not only produces better-quality images but also achieves a higher compression ratio than conventional tree search vector quantization. When source coding is applied after VQ, the new model performs better than the generalized Lloyd algorithm in terms of distortion, bits per pixel, and encoding complexity for low-detail and medium-detail images.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An integrated circuit design for pruned tree-search vector quantization encoding with an off-chip controller

The design of an encoder for pruned tree-search vector quantization (VQ) is discussed. This allows near-optimal performance in a mean square error sense while keeping the hardware complexity low. The encoder is partitioned into a slave processor chip that computes the distance and performs minimizations and an off-chip controller that directs the search. Pointer addressing is exploited in the codebook memory to keep the controller hardware simple. Inputs to the slave processor include the source vectors, the code vectors; and external control signals. The slave processor outputs the index of the code vector that best approximates the input in a mean square error sense. The layout for the slave processor has been generated using a 1.2- mu m CMOS library and measures 5.76*6.6 mm/sup 2/. Critical path simulation with SPICE indicates a throughput of 89 million multiply-accumulates per second. This implies that real-time processing at MPEG rates can be achieved if the number of levels (N7) and the number of children at any node (M) obey the constraint M*N<32.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A comparison of vector quantization techniques in transform and subband coding of imagery

Transform image coding is considered using scalar quantization, pyramid vector quantization (PVQ), weighted pyramid vector quantization (WPVQ) and generalized Lloyd algorithm vector quantization (VQ). Subband image coding is considered using scalar quantization and pyramid vector quantization. A modification to the PVQ encoding algorithm is described and shown to provide improvement in encoded image peak signal-to-noise ratio (PSNR) of 0.5 to 1.0 dB. The WPVQ encoding algorithm is described and an adaptive WPVQ image coder is developed which provides improvements of up to 1.5 dB over the PVQ coder. In a simulation study, at low bit-rates (0.5 bit/pel) the transform VQ coder generally provided the largest PSNR, although the PVQ subband coder was superior for some images, and th was competitive for most images. At larger encoding rates (for which, because of complexity, the generalized Lloyd algorithm VQ was not considered) the scalar quantizer subband coder generally yielded larger PSNR than did the scalar quantizer transform coder. The PVQ transform and subband coders provided similar values of PSNR, but the subband coded image had less blockiness than the transform coded image. At an encoding rate of 0.5 bit/pel, the subband coders and the full search VQ and adaptive WPVQ transform coders are roughly an order of magnitude more complex than the PVQ and tree-searched VQ transform coders.

Successive approximation vector quantization

AbstractThis paper presents a new “ordering” based suboptimal vector quantization, the successive approximation vector quantization (SAVQ). It is found that the SAVQ algorithm can be reduced to the bit arrangement mapping from vector to scalar followed by the scalar quantization. Therefore the SAVQ requires no calculation of distortion. However, the tree search vector quantization (TSVQ), another suboptimal VQ, does require calculation of distortion. This allows the real time vector quantizer to be implemented with significantly less hardware complexity than TSVQ. For the source with exponential density, the simulation results demonstrate that SAVQ provides the mean‐squared‐error equal to or less than that obtained by TSVQ.