Abstract: A method for adaptively equalizing sampled input picture element values in which an estimation error obtained from a sampled input picture element value and the estimation value thereof are adaptively quantized by selecting one of a plurality of quantization characteristics. Quantization steps are determined in accordance with the desired quantization characteristics. Input groups of picture elements are divided into adjacent blocks and a quantization characteristic used for succeeding blocks is determined according to the distribution of quantization level values of preceding blocks. A quantization characteristic selecting unit selects a quantization characteristic set by the quantization characteristic determining unit from among a plurality of quantization characteristics. The estimation errors are quantized in a predetermined block using the quantization characteristic which is so selected.

Abstract: An algorithm for the design of locally optimum vector quantizers relative to a distortion measure is used to design and simulate vector quantizers for both real sampled speech and for speech-like waveforms produced by a tenth order autoregressive random process with matching autocorrelation. Both squared-error and a weighted squared error were considered. The experimental results were compared with performance bounds from rate distortion theory based on the autoregressive model.

Abstract: Vector Quantization is applied to modify a 2400 bps LPC vocoder to operate at 800 bps, while retaining acceptable intelligibility and naturalness of quality. The design of this speech compression system is discussed and compared to other very low bit rate vocoders. Advantages of vector quantization over a scalar technique are examined in detail, and several new properties are presented.

Abstract: Three new techniques for designing and simulating low rate speech compression systems based on vector quantization (VQ) are described. The first is a rate-distortion speech coder that resembles a linear predictive coded (LPC) speech compression system, but has much lower rate (under 800 bits per second (bps)) and a much larger memory requirement. The encoder performs a minimum distortion rule using the Itakura-Saito distortion measure. The speech quality provided at such low rate is comparable to that of 2400 bps and 4800 bps standard LPC systems. The second system is a waveform coder consisting of a minimum (weighted and unweighted) mean-square error VQ of one or two bits per sample (6500 and 13000 bps, respectively). It can be considered as a multidimensional pulse code modulation (PCM) system. The speech quality provided is considered at least as good as that of other standard waveform coders. The third system combines ideas from the first two to obtain a residual-excited linear predictive (RELP) speech compression system using VQ in both model selection and residual digitization. The working rates of our RELP system are 7000 and 13500 bps providing, among the RELP systems that we know of, the best speech quality.

Abstract: In transmitting and storing signals and images, the information content of the signals and images must be compressed using sampling and quantization techniques. This paper examines a generalization of the sampling theorem permitting a realistic observation window and proposes a new inverse quantization method suitable for this generalized sampling and usual quantization.

Abstract: Combined quantization-detection of uncertain signals is considered. The distribution of the uncertain signal is not completely known, although it is known to belong to a finite set of possibilities. A simultaneous quantization-detection system consisting of a detector and a bank of quantizers is suggested. An example is presented for illustration.

Abstract: : We consider three problems in source coding First, we consider the composite source model A composite source has a switch driven by a random process which selects one of a possible set of subsources We derive some convergence results for estimation of the switching process, and use these to prove that the entropy of some composite sources may be computed some coding techniques for composite sources are also presented and their performance is bounded Next, we construct a variable-length-to-fixed-length (VL-FL) universal code for a class of unifilar Markov sources A VL-FL code maps strings of source outputs into fixed-length codewords We show that the redundancy of the code converges to zero uniformly over the class of sources as the blocklength increases The code is also universal with respect to the initial state of the source We compare the performance of this code to FL-VL universal codes We then consider universal coding for real-valued sources We show that given some coding technique for a known source, we may construct a code for a class of sources We show that this technique works for some classes of memoryless sources, and also for a compact subset of the class of k-th order Gaussian autoregressive sources (Author)

Abstract: I. Galilei invariant quantization The geometric quantization scheme enables one to quantize time dependent dynamics without introducing any reference frame. The resulting theory is equivalent to the one described by a time-dependent Schroedinger equation. II. Quantization of non-relativistic dynamics with spin Geometric quantization of a classical model of a particle with spin in an external electromagnetic field yields the Pauli theory of spin.