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.

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.

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.

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.

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.

This paper describes a very computationally efficient design algorithm for color image coding at low bit rates. The proposed algorithm is based on uniform tree-structured subband decomposition, multistage scalar quantization of the image subbands, and high order entropy coding. The main advantage of the algorithm is that no multiplications are required in both analysis/synthesis and encoding/decoding. This can lead to a simple hardware implementation of the subband coder, while maintaining a high level of performance.

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.

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.

Filter banks, subband/wavelets, and multiresolution decompositions that employ recursive filters have been considered previously and are recognized for their efficiency in partitioning the frequency spectrum. This paper presents an analysis of a new infinite impulse response (IIR) filter bank in which these computationally efficient filters may be changed adaptively in response to the input. The new filter bank framework is presented and discussed in the context of subband image coding. In the absence of quantization errors, exact reconstruction can be achieved. By the proper choice of an adaptation scheme, it is shown that recursive linear time-varying (LTV) filter banks can yield improvement over conventional ones.

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.>

The approach is based on some advances in the area of variable rate residual vector quantization considered separately, and in conjunction with subband image decomposition. Comparisons illustrate the improvement in performance attributable to this approach relative to the JPEG coding standard.>