Wilson C. Chung

An efficient motion estimation technique based on a rate-distortion criterion

We present an efficient motion estimation algorithm where motion vectors are selected based on a rate-distortion criterion. The algorithm minimizes the rate subject to a constraint on overall distortion by optimizing jointly the motion vector coder and the residual coder. As the joint optimization is generally very demanding, we propose methods where the rate-distortion performance of the residual coder is approximated by parametric functions. This results in a substantial reduction in computational complexity while sacrificing only a small loss in performance. Experimental results indicate that the proposed algorithm can improve the performance of H.261- and H.263-based video coders significantly while still generating H.261/H.263 decodable bit streams, respectively.

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.

Rate-distortion-constrained statistical motion estimation for video coding

A rate distortion constrained statistical motion estimation algorithm is presented that leads to improvements in subband video coding. The main advantages of the algorithm is that it requires a relatively small number of computations, produces a much smoother motion field, and employs a more effective measure of performance than the conventional mean absolute difference or mean squared error. The proposed algorithm circumvents problems in the motion compensation loop such as illumination variations, noise, and occlusions, by providing a mechanism for alternating between intraframe and residual coding. Experimental results demonstrate that the corresponding video coder outperforms the H.263 in terms of motion vector search complexity and overall bit rate at the same reproduction quality.

A new approach to scalable video coding

This paper introduces a new framework for video coding that facilitates operation over a wide range of transmission rates. The new method is a subband coding approach that employs motion compensation, and uses prediction-frame and intra-frame coding within the framework. It is unique in that it allows lossy coding of the motion vectors through its use of multistage residual vector quantization (RVQ). Furthermore, it selects the motion vector with the best rate-distortion tradeoff among a number of possible motion vector candidates, and provides a rate-distortion-based mechanism for alternating between intra-frame and inter-frame coding. The framework provides an easy way to control the system complexity and performance, and inherently supports multiresolution transmission.

A jointly optimized subband coder

The mainstream approach to subband coding has been to partition the input signal into subband signals and to code those signals separately with optimal or near-optimal quantizers and entropy coders. A more effective approach, however, is one where the subband coders are optimized jointly so that the average distortion introduced by the subband quantizers is minimized subject to a constraint on the output rate of the subband encoder. A subband coder with jointly optimized multistage residual quantizers and entropy coders is introduced and applied to image coding. The high performance of the coder is attributed to its ability to exploit statistical dependencies within and across the subbands. The efficiency of the multistage residual quantization structure and the effectiveness of the statistical modeling algorithm result in an attractive balance among the reproduction quality, rate, and complexity.

Subband image coding with jointly optimized quantizers

An iterative design algorithm for the joint design of complexity- and entropy-constrained subband quantizers and associated entropy coders is proposed. Unlike conventional subband design algorithms, the proposed algorithm does not require the use of various bit allocation algorithms. Multistage residual quantizers are employed because they provide greater control of the complexity-performance tradeoffs, and also because they allow efficient and effective high-order statistical modeling. The resulting subband coder exploits statistical dependencies within subbands, across subbands, and across stages, mainly through complexity-constrained high-order entropy coding. Experimental results demonstrate that the complexity-rate-distortion performance of the new subband coder is exceptional.

Image coding using high-order conditional entropy-constrained residual VQ

An extension of entropy-constrained residual vector quantization is presented where inter-vector dependencies are exploited. The method, which the authors call conditional entropy-constrained residual vector quantization, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors. The complexity of the proposed design algorithm is relatively low, due mainly to the efficiency of the multistage structure of the residual vector quantizer, but also to the effectiveness of the searching techniques used to locate the best conditioning spatial-stage region of support. Experimental results show that the new method outperforms standard entropy-constrained residual vector quantization while also requiring lower encoding complexity and memory requirements.<<ETX>>

Rate-distortion-constrained subband video coding

This paper introduces a subband video coding algorithm for operation over a continuum of rates from very low to very high. The key elements of the system are statistical rate-distortion-constrained motion estimation and compensation, multistage residual quantization, high order statistical modeling, and arithmetic coding. The method is unique in that it provides an improved mechanism for dynamic spatial and temporal coding. Motion vectors are determined in a nontraditional way, using a rate-distortion cost criterion. This results in a smoother and more consistent motion field, relative to that produced by conventional block matching algorithms. Control over the system computational complexity and performance may be exercised easily.

Spatially-varying IIR filter banks for image coding

The application of spatially variant IIR (infinite impulse response) filter banks to subband image coding is reported. The new filter bank is based on computationally efficient recursive polyphase decompositions that dynamically change in response to the input signal. In the absence of quantization, reconstruction can be made exact. However, by proper choice of an adaptation scheme, it is shown that subband image coding based on time-varying filter banks can yield improvement over the use of conventional filter banks. Simulation results are presented.<<ETX>>

Subband image coding with optimal intra- and inter-band subband quantization

Significant progress has been made in the theory and design of analysis/synthesis filter banks and in the independent encoding and decoding subsystems. However, by comparison, little attention has been given to the optimal design of subband quantizers in the sense of minimizing the overall average distortion subject to a constraint on the output rate of the subband system. In the paper, necessary conditions for optimal subband quantization are presented. These conditions can be employed to design quantizers that exploit the linear and non-linear dependencies both within and across the subbands, leading to improvement in performance.<<ETX>>