Showing papers by "William A. Pearlman published in 1994"

Conditional entropy-constrained vector quantization of displaced frame difference subband signals

Abstract: We study the performance of conditional entropy-constrained vector quantizers when used to quantize subbands of the displaced frame differences derived from video sequences. Chou and Lookabaugh (1990) originally suggested a locally optimal design of this new kind of vector quantizer which can be accomplished through a generalization of the well known entropy-constrained vector quantizer (ECVQ) algorithm. This generalization of the ECVQ algorithm to a conditional entropy-constrained is called CECVQ, i.e., conditional ECVQ. The non-memoryless quantization performed by the conditional entropy-constrained VQ is based on the current vector to be encoded and the previous encoded vector. A new algorithm for conditional entropy-constrained vector quantizer design is derived and it is based on the pairwise nearest neighbour technique presented by Equitz (1989). >

Conditional entropy-constrained vector quantization: high-rate theory and design algorithms

Abstract: The performance of optimum vector quantizers subject to a conditional entropy constraint is studied. This new class of vector quantizers was originally suggested by Chou and Lookabaugh (1990). A locally optimal design of this kind of vector quantizer can be accomplished through a generalization of the well-known entropy-constrained vector quantizer (ECVQ) algorithm. This generalization of the ECVQ algorithm to a conditional entropy-constrained is called CECVQ, i.e., conditional ECVQ. Furthermore, we have extended the high-rate quantization theory to this new class of quantizers to obtain a new high-rate performance bound. The new performance bound is compared and shown to be consistent with bounds derived through conditional rate-distortion theory. A new algorithm for designing entropy-constrained vector quantizers was introduced by Garrido, Pearlman, and Finamore (see IEEE Trans. Circuits Syst. Video Technol., vol.5, no.2, p.83-95, 1995), and is named entropy-constrained pairwise nearest neighbor (ECPNN). The algorithm is basically an entropy-constrained version of the pairwise nearest neighbor (ECPNN) clustering algorithm of Equitz (1989). By a natural extension of the ECPNN algorithm we develop another algorithm, called CECPNN, that designs conditional entropy-constrained vector quantizers. Through simulation results on synthetic sources, we show that CECPNN and CECVQ have very close distortion-rate performance. >

Texture coding using a Wold decomposition model

Abstract: A novel approach for coding textured images is presented. The texture field is assumed to be a realization of a regular homogeneous random field, which can have a mixed spectral distribution. On the basis of a 2D Wold-like decomposition, the field is represented as a sum of purely indeterministic, harmonic, and a countable number of evanescent fields. We present an algorithm for estimating and coding the texture model parameters, and show that the suggested algorithm yields high quality reconstructions at low bit rates. The model and the resulting coding algorithm are seen to be applicable to a wide variety of texture types found in natural images.