Distributed video codec (DVC) has been developed to construct a simple encoder that utilizes information theory for distributed sources in the circumstance of mobile multimedia communication. In the DVC codec, an efficient algorithm to generate side information (SI) is one of the most important techniques to improve the coding performance. We propose a scheme to increase the quality of SI frame, where the proposed scheme consists of three steps. In the first step, SI frame is constructed by motion estimation and motion compensation in the DVC decoder. Then, in the second step, the blocks in the temporary SI frame are classified into reliable or unreliable ones. The unreliable blocks are updated by block boundary matching algorithm in the third step. Simulation results show that the proposed algorithm outperforms the conventional methods significantly. In addition, the proposed scheme can be combined with the conventional schemes generating SI frame to increase the coding performance of the DVC codec.
We present a no-reference peak signal to noise ratio (PSNR) estimation algorithm based on discrete cosine transform (DCT) coefficient distributions from H.264/MPEG-4 part 10 advanced video codec (H.264/AVC) bitstreams. To estimate the PSNR of a compressed picture without the original picture on the decoder side, it is important to model the distribution of transform coefficients obtained from quantized coefficients accurately. Whereas several conventional algorithms use the Laplacian or Cauchy distribution to model the DCT coefficient distribution, the proposed algorithm uses a generalized Gaussian distribution. Pearson's χ2 (chi-square) test was applied to show that the generalized Gaussian distribution is more appropriate than the other models for modeling the transform coefficients. The χ2 test was also used to find optimum parameters for the generalized Gaussian model. It was found that the generalized Gaussian model improves the accuracy of the DCT coefficient distribution, thus reducing the mean squared error between the real and the estimated PSNR.
We derive a modified version of the cubic convolution scaler to enlarge or reduce the size of digital images with arbitrary ratio. To enhance the edge information of the scaled image and to obtain a high-quality scaled image, the proposed scaler is applied along the direction of an edge. Since interpolation along the direction of an edge has to process nonuniformly sampled data, the kernel of the cubic convolution scalar is modified to interpolate the data. The proposed scaling scheme can be used to resize pictures in various formats in a transcoding system that transforms a bit stream compressed at one bit rate into one compressed at another bit rate. In many applications, such as transcoders, the resolution conversion is very important for changing the image size while maintaining high quality of the scaled image. We show experimental results that demonstrate the effectiveness of the proposed interpolation method. The proposed scheme provides clearer edges, without artifacts, in the resized image than do conventional schemes. The algorithm exhibits significant improvement in the minimization of information loss when compared with the conventional interpolation algorithms.
We derive a modified version of cubic convolution interpolation for the enlargement or reduction of digital images by arbitrary scaling factors. The proposed scaling scheme is used to resize various format pictures in the transcoding system, which transforms the bitstream compressed at a bit rate, such as the HD bitstream, into another bit rate stream, for example, the SD bitstream. The transcoding is performed in spatial domain. In many applications such as the transcoder, the resolution conversion is very important for changing the image size while the scaled image maintains high quality. The scaling process consists of two steps: fitting the original data with a continuous function, and resampling the function on a new sampling grid. We focus on the modification of the scaler kernel according to the relation between formats of the original and the resized image. In the modification, various formats defined in MPEG standards are considered. We show experimental results that demonstrate the effectiveness of the proposed interpolation method. The algorithm exhibits significant improvement in the minimization of information loss when compared with the conventional interpolation algorithms.
A coding technique, based on WT (wavelet transform) and TSVQ (tree-structured vector quantization), is proposed in this paper. Wavelet transformed image is composed of several subimages according to resolutions and edge directions, and has a particular PDF (probability density function), the generalized Gaussian distribution. We propose an improved tree- structured VQ coder based on the properties of wavelet transform. Edge information extracted from the subimages in the wavelet transform domain has been used to reduce the distortion. A new vector formation scheme and a new tree growing algorithm has been presented in this paper to reduce the distortion rate in the reconstructed image. Finally, in order to allow the receiver a picture as quickly as possible at minimum cost, we propose a progressive transmission scheme using unbalanced tree structured codebook. It is shown that unbalanced TSVQ is well adapted to progressive transmission. Simulations results indicate that the quality of the reconstructed image is excellent in the range of 0.30 - 0.40 bit/pixel.
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