Entropy-constrained Residual Vector Quantization Research Articles

Entropy-constrained reflected residual vector quantization: A realization of large block vector quantization

Vector quantization (VQ) techniques have been used for a number of years for developing successful data compression and classification algorithms. Vector quantization is inherently an unsupervised data clustering technique that smartly adopts to the given data statistics and is thus used in large variety of tasks. The gains associated with a VQ system are largely linked to the size of the block used. However, the complexity increases exponentially with an increase in block length. In conventional methods, a dimensional reduction pre-processing step is invoked before the VQ system in order to reduce the block length. For some data sources this may be unacceptable approach such as with hyper-spectral data. Therefore, a large block VQ is needed to fully exploit the linear and non-linear correlation in a data source. We propose an entropy-constrained reflected residual VQ (EC-RRVQ) as one alternative for large block vector quantization. The reflected residual VQ, a type of multistage VQ, has constrained structure with multiple stages having small-size code-books. The use of multiple code-books and a reflection constraint makes the computational complexity linearly depend on the number of stages involved. The linear increase in complexity prompted us to pose EC-RRVQ as a contender in the list of options of large block vector quantization implementation algorithms. Experimental results indicate that good image reproduction quality can be accomplished at relatively low bit rates. The performance of a 64-stage 16×16 EC-RRVQ at 0.175 bits per pixel is 23.75dB with 96 vector distortion calculations per source vector, while the number for previously proposed large-dimensional entropy-constrained residual VQ (EC-RVQ) designed under the same specifications is 21.17dB with 1212 vector distortion calculations per source vector.

Conditional entropy-constrained trellis-coded RVQ with application to image coding

This paper introduces an extension of conditional entropy-constrained RVQ (CEC-RVQ) that embodies trellis-coded quantization. The method, which we call conditional entropy-constrained trellis-coded residual vector quantization (CEC-TCRVQ), quantizes a supervector (made from a large number of neighboring vectors) to better extract the two-dimensional (2-D) correlation present in real images. Simulation results indicate that CEC-TCRVQ provides 0.3-0.4 dB improvement over CEC-RVQ for the 4/spl times/4 vector case and 1.3 dB improvement for the 8/spl times/8 case.

A fast PNN design algorithm for entropy-constrained residual vector quantization

A clustering algorithm based on the pairwise nearest-neighbor (PNN) algorithm developed by Equitz (1989), is introduced for the design of entropy-constrained residual vector quantizers. The algorithm designs residual vector quantization codebooks by merging the pair of stage clusters that minimizes the increase in overall distortion subject to a given decrease in entropy. Image coding experiments show that the clustering design algorithm typically results in more than a 200:1 reduction in design time relative to the standard iterative entropy-constrained residual vector quantization algorithm while introducing only small additional distortion. Multipath searching over the sequence of merges is also investigated and shown experimentally to slightly improve rate-distortion performance. The proposed algorithm can be used alone or can he followed by the iterative algorithm to improve the reproduction quality at the same bit rate.

Conditional entropy-constrained residual VQ with application to image coding

This paper introduces an extension of entropy constrained residual vector quantization (VQ) where intervector dependencies are exploited. The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors. When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements, moreover, it can be designed to support progressive transmission in a natural way. It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature. This is due partly to the joint optimization between the residual vector quantizer and a high order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.

Image coding using entropy-constrained residual vector quantization

An entropy-constrained residual vector quantization design algorithm is used to design codebooks for image coding. Entropy-constrained residual vector quantization has several important advantages. It can outperform entropy-constrained vector quantization in terms of rate-distortion performance, memory, and computation requirements. It can also be used to design vector quantizers with relatively large vector sizes and high output rates. Experimental results indicate that good image reproduction quality can be achieved at relatively low bit rates. For example, a peak signal-to-noise ratio of 30.09 dB is obtained for the 512x512 LENA image at a bit rate of 0.145 b/p.

Subband image coding using entropy-constrained residual vector quantization

Subband image coding using entropy-constrained residual vector quantization

Adaptive entropy-constrained residual vector quantization

Entropy-constrained residual vector quantization (EC-RVQ) is a relatively new VQ method that was shown to be capable of excellent rate-distortion performance. The paper investigates the incorporation of adaptivity into the EC-RVQ framework for application to image coding. Experimental results show that the dynamic nature of the implementation leads to a noticeable improvement in coding quality as well as improved robustness. >