A clustering algorithm for the design of efficient vector quantizers to be followed by entropy coding is proposed. The algorithm, called entropy-constrained pairwise nearest neighbor (ECPNN), designs codebooks by merging the pair of Voronoi regions which gives the least increase in distortion for a given decrease in entropy. The algorithm can be used as an alternative to the entropy-constrained vector quantizer design (ECVQ) proposed by Chou, Lookabaugh, and Gray (1989). By a natural extension of the ECPNN algorithm the authors develop another algorithm that designs alphabet and entropy-constrained vector quantizers and call it alphabet- and entropy-constrained pairwise nearest neighbor (AECPNN) design. Through simulations on synthetic sources, it is shown that ECPNN and ECVQ have indistinguishable mean-square-error versus rate performance and that the ECPNN and AECPNN algorithms obtain as close performance by the same measure as the ECVQ and AECVQ (Rao and Pearlman, 1993) algorithms. The advantages over ECVQ are that the ECPNN approach enables much faster codebook design and uses smaller codebooks. A single pass through the ECPNN (or AECPNN) design algorithm, which progresses from larger to successively smaller rates, allows the storage of any desired number of intermediate codebooks. In the context of multirate subband (or transform) coders, this feature is especially desirable. The performance of coding image pyramids using ECPNN and AECPNN codebooks at rates from 1/3 to 1.0 bit/pixel is discussed.>

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.>

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).>

A maximum-likelihood (ML) estimation procedure for the parameters of homogeneous random fields is presented. On the basis of a 2D Wold-like decomposition, the random field is decomposed into a sum of mutually orthogonal components of three types: purely indeterministic, harmonic, and generalized evanescent. Because, in general, a homogeneous random field is characterized by a mixed spectral distribution, parameter estimation techniques which are solely based on spectral density estimators are not adequate. An algorithm which results in a complete parameter estimation for the purely indeterministic and harmonic components of the homogeneous random field and in estimates for the evanescent component frequencies is presented. The new ML method produces highly accurate estimates of these parameters.>

A new algorithm for multirate vector quantization is used for coding image pyramids. The algorithm, called alphabet and entropy-constrained pairwise-nearest-neighbor (AECPNN), designs codebooks choosing subcodebooks from a large generic codebook. The algorithm is the natural extension of the ECPNN design technique with constrained alphabet. Results of coded image pyramids obtained using the present algorithm are comparable to results for the ECPNN design technique.>

A clustering algorithm for the design of efficient vector quantizers to be followed by entropy coding is proposed. The algorithm generates a sequence of quantizers whith rates close to theoretical rate- distortion bound. A fast version of this algorithm can be used as an alternative to the entropy-constrained vector quantizer technique proposed by Chou, Lookabaugh and Gray.