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In this work, we present a coding scheme based on a rate-distortion optimum wavelet packets decomposition and on an adaptive coding procedure that exploits spatial non-stationarity within each subband. We show, by means of a generalization of the concept of coding gain to the case of non-stationary signals, that it may be convenient to perform subband decomposition optimization in conjunction with intraband optimal bit allocation. In our implementation, each subband is partitioned into blocks of coefficients that are coded using a geometric vector quantizer with a rate determined on the basis of spatially local statistical characteristics. The proposed scheme appears to be simpler than other wavelet packets-based schemes presented in the literature and achieves good results in terms of both compression and visual quality.

Multiresolution techniques have become more and more appealing in current image coding. Image multispectral representation produces many important features such as spectral shaping of coding noise according to human eye perception, good image energy compaction, coder tuning with respect to any band characteristics, and allows for multilevel layered transmission that is one of the main targets pursued by the broadcaster. Despite these appealing capabilities, multiresolution techniques have failed to give the expected results. One of the reasons for this failure is the difficulty of exploiting the temporal redudancy present in image sequences. This paper addresses the problem of motion compensation in a multiresolution environment, considering both QMF-SBC and wavelet transform approaches. Different motion compensation schemes are derived and their efficiency is considered with regard to scalability and to the lengths of subband analysis and synthesis filters. Simulation results are used to support relevant conclusions where needed.

The authors suggest a parallel processing structure using the proposed international standard for visual telephony (CCITT P*64 kbs standard) as processing elements, to compress digital high definition television (HDTV) pictures. The basic idea is to partition an HDTV picture, in space or in frequency, into smaller sub-pictures and then compress each sub-picture using a CCITT P*64 kbs coder. This seems to be a cost-effective solution to the HDTV hardware. Since each sub-picture is processed by an independent coder, without coordination these coded sub-pictures may have unequal picture quality. To maintain a uniform quality HDTV picture, the following two issues are studied: sub-channel control strategy (bits allocated to each sub-picture); and quantization and buffer control strategy for individual sub-picture coders. Algorithms to resolve these problems and their computer simulations are presented. >

A novel architecture for image compression is proposed, which is based on a suitable combination of subband filtering and linear neural networks. This combination permits efficient coding, together with the advantages of the neural-network-based approach. The architecture is described, and results of simulations are presented. The architecture is shown to perform well, notwithstanding the reduced complexity of the approach. The structure is highly parallel, so that high computation rates are possible; this property can be useful if sequences of images are to be compressed. >

In recent years the ``Internet Multicast Backbone'', or MBone, has risen from a small, research curiosity to a large scale and widely used communications infrastructure. A driving force behind this growth was the development of multipoint audio, video, and shared whiteboard conferencing applications. Because these real-time media are transmitted at a uniform rate to all the receivers in the network, a source must either run at the bottleneck rate or overload portions of its multicast distribution tree. We overcome this limitation by moving the burden of rate-adaptation from the source to the receivers with a scheme we call Receiver-driven Layered Multicast, or RLM. In RLM, a source distributes a hierarchical signal by striping the different layers across multiple multicast groups and receivers adjust their reception rate by simply joining and leaving multicast groups. In this paper we describe a layered video compression algorithm which, when combined with RLM, provides a comprehensive solution for scalable multicast video transmission in heterogeneous networks. In addition to a layered representation, our coder has low-complexity (admitting an efficient software implementation) and high loss resilience (admitting robust operation in loosely controlled environments like the Internet). Even with these constraints, our hybrid DCT/wavelet-based coder exhibits good compression performance. It outperforms all publicly available Internet video codecs while maintaining comparable run-time performance. We have implemented our coder in a ``real'' application --- the UCB/LBL video conferencing tool ``vic.'' Unlike previous work on layered video compression and transmission, we have built a fully operational system that is currently being deployed on a very large scale over the MBone.

Video coding has been investigated for the novel application of video transmission over packed-switched networks. The underlying design goals are presented, together with a software implementation of a coding scheme, which divides the input signal into frequency bands in all three dimensions, seems promising in that it lends itself to parrallel implementation, is robust enough to handle errors due to lost packets, and yields high compression with sustained good quality. Moreover, it may be well integrated with the network to handle issues is given, together with the results obtained from simulated system.

With the increasing use of multimedia technologies, image compression requires higher performance as well as new features. To address this need in the specific area of still image encoding, a new standard is currently being developed, the JPEG2000. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and functionalities that current standards can either not address efficiently or in many cases cannot address at all. Lossless and lossy compression, embedded lossy to lossless coding, progressive transmission by pixel accuracy and by resolution, robustness to the presence of bit-errors and region-of-interest coding, are some representative features. It is interesting to note that JPEG2000 is being designed to address the requirements of a diversity of applications, e.g. Internet, color facsimile, printing, scanning, digital photography, remote sensing, mobile applications, medical imagery, digital library and E-commerce.

This paper introduces a concept of analog-to-digital conversion for very high-speed applications. Like the time-interleaving conversion, this method uses different conventional ADCs (channels), which work in parallel, but the approach is different. The basic principle is close to techniques used in signal processing, such as the discrete Fourier transform (DFT) and sub-band coding. Because of its low sensitivity to sampling-clock jitter; this technique can achieve a 6 bit effective accuracy up to the Nyquist frequency.

In this paper we review some recent interactions between harmonic analysis and data compression. The story goes back of course to Shannon's R(D) theory in the case of Gaussian stationary processes, which says that transforming into a Fourier basis followed by block coding gives an optimal lossy compression technique; practical developments like transform-based image compression have been inspired by this result. In this paper we also discuss connections perhaps less familiar to the information theory community, growing out of the field of harmonic analysis. Recent harmonic analysis constructions, such as wavelet transforms and Gabor transforms, are essentially optimal transforms for transform coding in certain settings. Some of these transforms are under consideration for future compression standards. We discuss some of the lessons of harmonic analysis in this century. Typically, the problems and achievements of this field have involved goals that were not obviously related to practical data compression, and have used a language not immediately accessible to outsiders. Nevertheless, through an extensive generalization of what Shannon called the "sampling theorem", harmonic analysis has succeeded in developing new forms of functional representation which turn out to have significant data compression interpretations. We explain why harmonic analysis has interacted with data compression, and we describe some interesting recent ideas in the field that may affect data compression in the future.