Showing papers by "William A. Pearlman published in 1984"
TL;DR: Performance near the rate-distortion bound is achievable using a reproduction alphabet consisting of a small number of optimum quantizer levels, given reasonably chosen fixed sets of reproduction letters and/or their probabilities.
Abstract: Constraining the reproduction alphabet to be of small size in encoding continuous-amplitude memoryless sources has been shown to give very small degradation from the ideal performance of the rate-distortion bound. The optimum fixed-size reproduction alphabet and its individual letter probabilities are required in order to encode the source with performance approaching that of theory. These can be found through a somewhat lengthy, but convergent, algorithm. Given reasonably chosen fixed sets of reproduction letters and/or their probabilities, we define new rate-distortion functions which are coding bounds under these alphabet constraints. We calculate these functions for the Gaussian and Laplacian sources and the squared-error distortion measure and find that performance near the rate-distortion bound is achievable using a reproduction alphabet consisting of a small number of optimum quantizer levels.
23 citations
TL;DR: It is concluded that in a medium rate range below 1 bit/pel/frame where reconstructions for hybrid transform/ DPCM may be unsatisfactory, there is enough margin for improvement to consider more sophisticated coding schemes.
Abstract: We seek to evaluate the efficiency of hybrid transform/ DPCM interframe image coding relative to an optimal scheme that minimizes the mean-squared error in encoding a stationary Gaussian image sequence. The stationary assumption leads us to use the asymptotically optimal discrete Fourier transform (DFT) on the full frame of an image. We encode an actual image sequence with full-frame DFT/DPCM at several rates and compare it to previous interframe coding results with the same sequence. We also encode a single frame at these same rates using a full-frame DFT to demonstrate the inherent coding gains of interframe transform DPCM over intraframe coding. We then generate a pseudorandom image sequence with precise Gauss-Markov statistics and encode it by hybrid full-frame DFT/DPCM at various rates. We compare the signal-to-noise ratios (SNR's) of these reconstructions to the optimal ones calculated from the rate-distortion function. We conclude that in a medium rate range below 1 bit/pel/frame where reconstructions for hybrid transform/ DPCM may be unsatisfactory, there is enough margin for improvement to consider more sophisticated coding schemes.
19 citations
01 Mar 1984
TL;DR: A suboptimal discrete cosine transform is used to encode image sub-blocks and a selective search through the code tree using a transform tree coding technique that is theoretically optimal for Gaussian sources and the squared error criterion at all nonzero rates.
Abstract: We have utilized for image coding a transform tree coding technique that is theoretically optimal for Gaussian sources and the squared error criterion at all nonzero rates. In the interest of affordable computation, we used a suboptimal discrete cosine transform to encode image sub-blocks and a selective search through the code tree. Coding simulations of a woman's face image at rates of 1.0 and 0.25 bits/pel gave good to excellent results. The rate 1.0 results were superior to previous ones using another tree coding method. Until now there have been no reported results with searched codes for images using a rate under 1.0 bit/pel. Encoding a computer-generated image source indicated SNR performance about 2dB less than the optimal SNR of the rate-distortion bound.
3 citations
04 Dec 1984
TL;DR: Pearman and Song as mentioned in this paper proposed a robust method for restoring low light level images degraded by alinear space-invariant blur using a constrained linear least squares technique where the error of the first step is treated as additive noise.
Abstract: A robust method for restoration of photon- limited, blurred imagesWilliam A. Pearlman and Woo -Jin SongElectrical, Computer, and Systems Engineering DepartmentRensselaer Polytechnic InstituteTroy, N.Y. 12180AbstractA two -step procedure is developed for restoring low light level images degraded by alinear space- invariant blur. The first step uses a linear minimum mean square point esti-mate of the blurred image. The second removes the blur through a constrained linear leastsquares technique where the error of the first step is treated as additive noise. No priorknowledge of the object is required, as the procedure utilizes sample statistics in an anal-ysis window around each received image element to develop the estimators. The size of thewindow is adaptive, as it is adjusted for the next image element according to an activityindex computed for the current window. In experiments with simulated photon- noise -degraded,lineal- motion -blurred images, the efficacy of the procedure is demonstrated visually andmeasured by signal -to -noise ratio improvements.IntroductionA fundamental limitation in restoring images at low light levels is the photon noisearising from the random number of photoelectron counts during the observation interval foreach detector in the image detector array. Moreover, the image may also have suffered de-gradation in transmission through the atmosphere and in reception by a diffraction -limitedoptical system which may be moving relative to the object (image source). Here these atmos-pheric and optical system degradations shall be represented as a linear and spatially invar-iant filtering of the object. This paper addresses the problem of obtaining object estimatesfrom predominantly photon -limited images which have undergone lineal motion blur. These es-timates are obtained without prior knowledge of object or image statistics, as sample sta-tistics are generated upon reception and adaptively updated for different regions of the
2 citations