Showing papers on "Peak signal-to-noise ratio published in 1998"

Approximation of calculations for forward discrete cosine transform

Abstract: This paper presents new schemes to reduce the computation of the discrete cosine transform (DCT) with negligible peak-signal-to-noise ratio (PSNR) degradation. The methods can be used in the software implementation of current video standard encoders, for example, H.26x and MPEG. We investigated the relationship between the quantization parameters and the position of the last nonzero DCT coefficient after quantization. That information is used to adaptively make the decision of calculating all 8/spl times/8 DCT coefficients or only part of the coefficients. To further reduce the computation, instead of using the exact DCT coefficients, we propose a method to approximate the DCT coefficients which leads to significant computation savings. The results show that for practical situations, significant computation reductions can be achieved while causing negligible PSNR degradation. The proposed method also results in computation savings in the quantization calculations.

Scalable subband image coding with segmented orthogonal matching pursuit

Abstract: In this paper, a novel algorithm for low bit-rate image compression is presented. In this technique, we use a new image representation algorithm called segmented orthogonal matching pursuit (SOMP) (Rabiee and Kashyap, 1998) to encode the subbands of an image. Our preliminary results show that our algorithm performs better than the segmentation based matching pursuit (QTMP) (Rabiee et al. 1996) and EZW (Shapiro 1993) encoders at lower bit rates, based on subjective image quality and peak signal-to-noise ratio (PSNR).

On Color Morphology

Abstract: In this paper, a new color morphology for color image processing is proposed as an extension of gray-scale morphology, in which a vector norm is used as a measure of magnitude of a color vector. Some properties of color morphological operations are analyzed and discussed in relation with gray-scale morphology. Experimently compared the performances of noise filterings and edge detections for several color and gray-scale morphological operations. As results of noise filterings. the basic dual color morphological operations of erosion and dilation are slightly better than the corresponding separate gray-scale morphological operations, while the other color morphological filters (CMFs) are very similar to the corresponding separate gray-scale morphological filters (SGMFs) with respect to the peak signal to noise ratio (PSNR). And in edge detection, the CMFs show better performances than both SGMFs and the conventional top hat transform of gray image.

Mobile videophone systems for radio speech channels

Abstract: A suite of bandwidth efficient image codecs are presented for the use in second-generation wireless systems, such as the American IS-54 and IS-95 systems, the Pan-European GSM system and the Japanese digital cellular system. The proposed codecs are configured to operate at 9.6K bits per second and are suitable for Quarter Common Intermediate Format videophone sequences, scanned at 10 frames per second. The new image codecs employ the orthonormal wavelet transform to decompose the displaced frame difference data for each frame, into four frequency subbands. The wavelet coefficients within the frequency subbands are then encoded using vector quantization. Comparison measures are undertaken for the two-stage pairwise nearest neighbor (PNN) algorithm and the designs are rated upon their ability to coherently reconstruct an efficient codebook from a training sequence of vector coefficients. It was found that the two- stage PNN algorithm constitutes a valuable compromise in terms of computational complexity with only negligible performance loss. When the codecs were configured to operate at 9.6Kpbs, the average peak signal to noise ratio of the two stage PNN and the adaptive algorithms were in excess of 28kB and 30dB respectively.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Predictive-transform residual vector quantization

Abstract: This work investigates a new compression scheme that incorporates rate-constrained residual vector quantization (RVQ) of the transform coefficient error vectors arising in a minimum mean squared error (MMSE) predictive-transform (PT) methodology. The proposed technique that we call predictive-transform residual vector quantization (PTRVQ) exploits the correlation between neighboring blocks by predicting the transform coefficients of each block using information from previously encoded blocks. PTRVQ, therefore, exploits both the intra- and inter-block correlation as well as employs dimensionality reduction in the transform domain in order to generate a highly compact code. Experimental results obtained with several standard monochrome images and compression ratios as high as 135:1 demonstrate quite noticeable improvements of the reconstructed images in both perceptual quality and peak signal to noise ratio (PSNR). The PSNR improvements are as high as 1.4 dB over a wavelet based coder.

Optimized source coding and coded modulation for digital transmission of still images on a noisy channel

Abstract: In this paper, we propose a complete transmission system for still images which minimizes the distortion introduced by residual transmission errors. This system comprises a source encoder combining DCT (discrete cosine transform) and VQ (vector quantization). The compression rate is equal to 16.7 for an average PSNR (peak signal to noise ratio) of 29.2 dB. The data are then encoded by a BCH (63,57,3) block code and mapped on a 64-QAM constellation. The parameters of the code book and channel encoder have been optimized for 64-QAM. For channel decoding, we use maximum likelihood sequence decoding. The PSNR of the reconstructed image in the receiver is equal to 28.6 dB for a channel SNR of 11 dB. This represents a very small degradation of the PSNR (0.6 dB) in presence of a SER (symbol error rate) of 10% at the input of the receiver.

Restoring Image Quality Through Structure Preserving De-noising

Abstract: In many image transmission and acquisition situations, the image may become corrupted by additive noise. De-noising refers to the process of removing the noise while maintaining good visual quality. This problem has assumed major significance with the increase in image related communication that has accompanied the exponential growth of the internet. Traditionally, image quality is measured in terms of PSNR (Peak Signal to Noise Ratio) which may have limited relation, at best, to the perceptual quality of the image. In this paper we present a novel de-noising scheme which results in significantly improved performance in terms of both perceptual quality and PSNR. Furthermore, we show that the de-noising framework that we propose encompasses the usual linear transform based de-noising schemes as special cases.

Wavelet based adaptive vector quantization for encoding large color images

Abstract: The generation of multi-resolution codebooks for encoding and decoding large (2K/spl times/1K) color images is, in general, a challenging task, due to the long search processes involved. We have used an adaptive clustering technique for generating multi-resolution codebooks by vector quantization of wavelet-transformed sub-images up to four levels of decomposition in an efficient manner. Using a combination of clustering up to the fourth level and selective retention of sub-images at the first and second levels, images of varying qualities can be reconstructed from a multi-resolution codebook. The histograms of the reconstructed images demonstrate little or no change from the histogram of the original, even up to a compression ratio of 132:1. The peak signal-to-noise ratio and the mean square error are also better than those from other benchmark compression techniques.

Ordered statistics decoding of linear block codes for robust H.263 video transmission in AWGN channel

Abstract: Soft-decision decoding of linear block codes for robust H.263 video transmission in a zero-mean, additive white Gaussian noise (AWGN) channel is investigated. We implement an effective error concealment (EC) scheme at the source decoder to reduce the annoying artifacts caused by decoding a corrupted bit stream. To alleviate the spatial and temporal error propagation, an error prevention (EP) strategy is introduced at the H.263 encoder. Simulation results show that a large portion of the peak signal-to-noise ratio (PSNR) gain is obtained by ordered statistics decoding of the received sequence. Furthermore, the residual channel coding errors are concealed and compensated by realizing the proposed EC and EP schemes.

Postprocessing for image coding applications using neural network visual model

Abstract: We present a neural network visual model (NNVM) which extracts multi-scale edge features from the decompressed image and uses these visual features as input to estimate and compensate the coding distortions. Our approach is a generic postprocessing technique and can be applied to all the main coding methods. Experimental results involving post-processing four coding systems show that the NNVM significantly improves the quality of reconstructed images, both in terms of the objective peak signal to noise ratio and subjective visual assessment.

On the influence of the phase of conjugate quadrature filters in subband image coding

Abstract: The influence of the phase of conjugate quadrature filters (CQFs) on the performances of a subband coding scheme is analyzed. When the filter length is short, the phase characteristic has virtually no influence on the peak signal-to-noise ratio (PSNR)/bit rate results and on the performance of postprocessing algorithms such as edge detection.