Optimum time-frequency decompositions are very useful in audio coding applications, because the signal energy can be maximally concentrated even for the wide variety of audio signal characteristics. Moreover, this signal representation is particularly well suited for a perceptual weighting of the quantization noise. The well known tree structure of cascaded 2-channel filterbanks allows a very flexible optimization, leading to a signal adaptive, dynamic wavelet packet decomposition. A major drawback of this technique are strong spectral side lobes which produce clearly audible aliasing in perceptual coders. We present a new dynamic wavelet packet decomposition, based on modulated lapped transforms, which allows the same flexibility while avoiding the disadvantage mentioned above. We propose a scheme for low bit rate audio coding that efficiently exploits the high energy concentration. This new codec yields excellent audio quality at about 55 kb/s for monophonic signals.

Current and future visual communications for applications such as broadcasting videotelephony, video- and audiographic-conferencing, and interactive multimedia services assume a substantial audio component. Even text, graphics, fax, still images, email documents, etc. will gain from voice annotation and audio clips. A wide range of speech, wideband speech, and wideband audio coders is available for such applications. In the context of audiovisual communications, the quality of telephone-bandwidth speech is acceptable for some videotelephony and videoconferencing services. Higher bandwidths (wideband speech) may be necessary to improve the intelligibility and naturalness of speech. High quality audio coding including multichannel audio will be necessary in advanced digital TV and multimedia services. This paper explains basic approaches to speech, wideband speech, and audio bit rate compressions in audiovisual communications. These signal classes differ in bandwidth, dynamic range, and in listener expectation of offered quality. It will become obvious that the use of our knowledge of auditory perception helps minimizing perception of coding artifacts and leads to efficient low bit rate coding algorithms which can achieve substantially more compression than was thought possible only a few years ago. The paper concentrates on worldwide source coding standards beneficial for consumers, service providers, and manufacturers.>

We present a new image coding scheme based on an unification of fractal and transform coding. We introduce a generalization of the luminance transformation generally used by fractal coding schemes. By extending the luminance transformation to the frequency domain fractal and transform coding become subsets of the proposed transformation. Our new coding scheme FTC (fractal based transform coding) combines the advantages of both techniques. Compared to JPEG a coding gain of 1.5-2.5 dB (PSNR) is obtained. The encoding time is reduced compared to conventional fractal coding schemes and a better convergence at the decoder is attained. At equal error rates the subjective quality of images coded with the new scheme is superior compared to transform coded images.>

A full-motion video coder based on a threedimensional subband framework is developed. The original image data is decomposed into different spatial-temporal frequency bands and based on the characteristics of the data in each band, different quantization strategies are applied to each band. The lowest spatial-temporal frequency band is coded using a three dimensional switched predictor and optimum scalar quantizer. The nondominant subbands which contain perceptually significant data are coded using a vector quantization approach in which the sparse, highly structured nature of the data in the higher-frequency subbands is exploited in the design of the codebook.>

This paper deals with the application of multipath search coding (MSC) concepts to the coding of stationary memoryless and correlated sources and of speech signals at a rate of one bit per sample. We have made use of three MSC classes: 1) codebook coding (vector quantization), 2) tree coding, and 3) trellis coding. This paper explains the performances of these coders and compares them both with those of conventional coders and with rate-distortion bounds. Figs. 2 and 3 demonstrate the potentials of MSC coding strategies. The paper reports also on results of MSC coding of speech, where both the strategy of adaptive quantization and of adaptive prediction were included in coder design.

This paper deals with the application of tree and trellis coding concepts to the coding of stationary first-order auto-regressive sources and speech signals at a rate of 1 bit/sample. We show first that a probabilistically generated tree (or trellis) performs significantly better than a tree (or trellis) that is generated in a deterministic way. We show then that the segmental signal-to-noise-ratios of conventional speech coding schemes (including) those with adaptive quantization and/or prediction) can be increased by about 2 dB. Slightly higher values of 3 to 4 dB have been realized with schemes which employ backward-adaptive quantization.

This paper presents the results of a pilot study comparing four different speech waveform coding techniques of varying complexity. Coder transmission rates of 24, 16, and 9.6 Kb/s were used in the experiment. Subjective ratings and objective measurements of quality are obtained and compared. A number of conclusions are drawn concerning the quality and complexity, of different coding techniques. By comparing the objective measurements to the subjective ratings a number of conclusions are also drawn concerning the strengths and weaknesses of various (objective) quality measures of speech waveform coders.

This paper investigates the optimum entropy versus distortion performance of quantizers optimized for uniform, Gaussian, Laplacian, and gamma-distributed memoryless sources which are useful models of the quantizer input signals in speech or picture coding schemes. We list the maximally obtainable signal-to-quantization noise ratios for one-dimensional optimum (i.e. entropy-coded) quantizers in the important low bit-rate region. These results have been obtained by an iterative solution of a set of nonlinear equations. Additionally we have also computed the corresponding rate-distortion functions by employing the Blahut-algorithm. These latter results upperbound the performances of multi-dimensional quantization schemes, and a comparison with the former results indicates the penalty to be paid for restricting a coder to perform a one-dimensionai quantization. It will be shown that the differences can be significant in the low bit-rate region.

Current scientific efforts in the field of digital processing of speech are focused at the aims of improving the efficiency in the present state of the art, and of developing new digital speech communication systems. Therefore, thorough studies on the statistical characteristics of speech signals, speech coding, speech recognition, and speech synthesis are necessary. Recent results and actual trends are reviewed in this paper.