Showing papers by "William A. Pearlman published in 1985"
TL;DR: A theorem that guarantees the existence of an optimal code for any code rate using such a tree is proved and uses the random coding argument in conjunction with a theorem on survival of a branching process with random environment.
Abstract: A new tree code is introduced for discrete-time stationary Gaussian sources with hounded, integrable power spectra and the squared-error distortion measure. The codewords in the tree are reconstructions of Karhunen-Loeve transforms of the source words. The branching factor and the number of code letters per branch may vary with level in the tree. A theorem that guarantees the existence of an optimal code for any code rate using such a tree is proved. The proof uses the random coding argument in conjunction with a theorem on survival of a branching process with random environment. A suboptimal but computationally affordable realization of the theorem's coding technique was used for encoding simulations for six autoregressive sources at rates of 1.0, 0.50, 0.25 , and 0.10 bits per source symbol. The average distortion results were generally within 1 dB of the distortion-rate bound but varied widely depending on the source and rate. The results were compared with those for transform quantization simulations for the same sources and rates. The tree code always performed better but only by an average of 0.44 dB all sources and rates. Longer source blocks and more intensive search would certainly improve the performance of the tree codes, but at the expense of extra computation and storage.
15 citations
26 Apr 1985
TL;DR: In encoding simulations the adaptation with smoothing and quantization gave better performance with a block size of 16 at lower search intensity and with more uniformity and less blocking effect, but with a savings of search and computational complexity.
Abstract: A transform tree code, theoretically optimal for stationary Gaussian sources and the squared-error distortion measure at all rates, is adaptively applied in a blockwise fashion to the encoding of images. The rate of the basic tree code is 0.5 bits/pel and the overhead rate ranges from .24 to .36 bits/pel. A smoothing and block quantization is used for effective reduction of the rate for transmitting a set of eigenvalue estimates, which comprise the main contribution to the overhead. In encoding simulations the adaptation with smoothing and quantization gave better performance with a block size of 16 at lower search intensity and with more uniformity and less blocking effect. At a block size of 32, where the reconstruction quality is higher, the same adaptation technique yielded nearly the same results as the non-adaptive one at the same overall rate, but with a savings of search and computational complexity.
4 citations