Showing papers by "William A. Pearlman published in 1990"

Adaptive cosine transform image coding with constant block distortion

Abstract: An adaptive block discrete-cosine transform (DCT) coding scheme is implemented with the same average distortion designated for each block. This constant distortion designation not only has perceptual advantages, but also allows the rate to vary, adjusting to the changing spectral characteristics among the blocks. The successful execution of this scheme requires a different spectral estimate for each block. To keep overhead and computation within limits, a novel technique is introduced by which a two-dimensional block spectrum is characterized by a one-dimensional autoregressive model. Simulations with images of natural scenes and medical radiology provide reconstructions with nearly uniform block distortion and very high visual and measurable quality at low rates. >

Clustering algorithm for entropy-constrained vector-quantizer design

Abstract: 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 ratedistortion 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.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Adaptive estimators for filtering noisy images

Abstract: A new estimation criterion called the minimum-error minimum correlation (MEMC) criterion is implemented in conjunction with an adaptive windowing technique. The image statistics are calculated within the window, the dimensions of which vary depending on the local statistics. The MEMC estimator produces sharper and hence visually more pleasing restorations, while the adaptive windows tend to isolate regions of the image that are locally stationary. Since most of the error after restoration is in the vicinity of edges, a postprocessing step of filtering along the edges is applied. These image restorations are compared to both fixed and adaptive minimum mean squared error estimators.

Multiple-rate code book design for vector quantization of image pyramids

Abstract: A recently introduced tree growth algorithm the Marginal Returns (MR) algorithm is used to grow multiple rate tree structured vector quantizers for the pyramid coding of hexagonally sampled images. The use of a structured multi-rate code book solves two problems that normally arise in vector quantization of subbands. The multiple rate code book can operate over a wide range of rates thus dispensing with the need to transmit the code book as overhead while the tree structure reduces the search complexity. Search complexity is a crucial issue even in low rate pyramid coding since subbands with more information content are coded at high rates. In addition the design technique makes it possible to tune the coder to the spectral properties of the image by optimally allocating rate to the different subbands. It has been shown in an earlier paper that the Marginal Returns algorithm yields code books that are optimal for sources that meet the law of diminishing marginal returns. However even for sources that do not satisfy these conditions the algorithm gives coders that perform close to the optimal. Image coding results at rates below 1 bpp are presented.

Gain-adaptive trained transform trellis code for images

Abstract: There exists a transform trellis code that is optimal for stationary Gaussian sources and the squarederror distortion measure at all rates. In this paper we train an asymptotically optimal version of such a code to obtain one which is matched better to the statistics of real world data. The training algorithm uses the M-algorithm to search the trellis codebook and the LBG-algorithm to update the trellis codebook. To adapt the codebook for the varying input data we use two gain-adaptive methods. The gain-adaptive sheme 1 which normalizes input block data by its gain factor is applied to images at rate 0. 5 bits/pixel. When each block is encoded at the same rate the nonstationarity among the block variances leads to a variation in the resulting distortion from one block to another. To alleviate the non-uniformity among the encoded image we design four clusters from the block power in which each cluster has its own trellis codebook and different rates. The rate of each cluster is assigned through requiring a constant distortion per-letter. This gain-adaptive scheme 2 produces good visual and measurable quality at low rates.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.