Filter banks and wavelet 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 filter bank is presented and discussed in the context of finite-support signals with the intended application in subband image coding. In the absence of quantization errors, exact reconstruction can be achieved and by the proper choice of an adaptation scheme, it is shown that IIR time-varying filter banks can yield improvement over conventional ones.

Subband coding is now one of the most important techniques for image compression. It was originally introduced by Crochiere in 1976, as a method for speech coding [CWF76]. Approximately a decade later it was extended to image coding by Woods and O’Neil [WO86] and has been gaining momentum ever since. There are two distinct components in subband coding: the analysis/synthesis section, in which filter banks are used to decompose the image into subband images; and the coding system, where the subband images are quantized and coded. A wide variety of filter banks and subband decompositions have been considered for subband image coding. Among the earliest and most popular were uniformband decompositions and octave-band decompositions [GT86, GT88, WBBW88, WO86, KSM89, JS90, Woo91]. But many alternate tree-structured filter banks were considered in the mid 1980s as well [Wes89, Vet84]. Concomitant with the investigation of filter banks was the study of coding strategies. There is great variation among subband coder methods and implementations. Common to all, however, is the notion of splitting the input image into subbands and coding these subbands at a target bit rate. The general improvements obtained by subband coding may be attributed largely to several characteristics—notably the effective exploitation of correlation within subbands, the exploitation of statistical dependencies among the subbands, and the use of efficient quantizers and entropy coders [Hus91, JS90, Sha92]. The particular subband coder that is the topic of discussion in this chapter was introduced by the authors in [KCS95] and embodies all the aforementioned attributes. In particular, intra-subband and inter-band statistical dependencies are exploited by a finite state prediction model. Quantization and entropy coding are performed jointly using multistage residual quantizers and arithmetic coders. But perhaps most important and uniquely characteristic of this particular system is that all components are designed together to optimize ratedistortion performance, subject to fixed constraints on computational complexity. High performance in compression is clearly an important measure of overall value,

This paper introduces a new HDTV coder based on motion compensation, subband coding, and high order conditional entropy coding. The proposed coder exploits the temporal and spatial statistical dependencies inherent in the HDTV signal by using intra- and inter-subband conditioning for coding both the motion coordinates and the residual signal. The new framework provides an easy way to control the system complexity and performance, and inherently supports multiresolution transmission. Experimental results show that the coder outperforms MPEG-2, while still maintaining relatively low 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.

A new approach to coding color images is presented in this paper, that provides high quality low bit rate performance with very modest computational complexity. The method is based on subband coding with residual multistage scalar quantizers. The high quality of the system is due mainly to the introduction of multidimensional prediction, which allows high order statistical dependencies to be exploited simultaneously among subbands, color planes, and quantization stages. This enables the system to perform at a level comparable to the best reported subband coders in the literature. However, unique to this coder is a very simple implementation that avoids multiplications in both the quantization and entropy coding stages. The paper provides a description of the new coder, a discussion of the design methodology, and some performance comparisons in terms of quality and complexity.

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.

This paper reports on the application of spatially variant IIR filter banks to subband image coding. 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, we show that subband image coding based on time varying filter banks can yield improvement over the use of conventional filter banks.

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.< >

A recent study on the application of entropy constrained residual vector quantization (EC-RVQ) to subband image coding is discussed. The newly introduced EC-RVQ has produced excellent performance results when applied directly to coding images, and is also more cost efficient than competing vector quantization (VQ) methods which have so far been reported in the literature. Experimental results show that subband coding with EC-RVQ performs very well, and place it among the best techniques presently available in terms of performance. A discussion of the new system is given, and some performance comparisons are presented.< >