Sharif Digital Repository

In this paper a new context modelling scheme for arithmetic coding of Motion Vectors (MVs) is proposed. The model uses the correlation between the horizontal and vertical components of MVs to improve the probability estimation of symbols. The accurate probability estimation can therefore improve the efficiency of the context-based arithmetic coder. The proposed scheme has been adapted to the H.264 advanced video codec and the simulation results show that a considerable bit rate saving can be achieved in MV coding  

This paper aims at applying H.264/AVC in medical video compression applications and improving its compression performance with higher perceptual quality and lower coding complexity. We propose a new method that uses lossless compression in the region of interest (ROI) and very high rate lossy compression in other regions. The propose method achieves a fast intra- and interprediction mode decision that is based on encountering coarse MBs for intra- and inter- prediction mode decision of the background region and fine MBs for the ROI region. The MBs of the background region are encoded with the maximum quantization parameter allowed by H.264/AVC in order to maximize the number of null... 

Motion estimation (ME) is one of the key parts of video compression algorithms. But, motion estimation and computation of motion vectors (MVs) are very time con-suming. In this paper, we propose a method for reducing the cost of motion estimation process. During this process, a series of candidate blocks should be searched to find the best motion vectors. In our method, we compare the skin parts of two blocks before comparing all pixel pairs of the two blocks. Having a preprocessing phase, the skin part comparison is performed quickly. This method provides a parameter that can be used to create a balance between the processing time and the motion estimation accuracy. © 2009 IEEE  

In this paper, the problem of spatial error concealment for real-time applications is addressed. The proposed method can be categorized in exemplar-based error concealment approaches. In this category, a patch of corrupted pixels are replaced by another patch of the image that contains correct pixels. For splitting the erroneous block to different patches, a novel context-dependent exemplar-based algorithm based on a previously proposed segmentation method is proposed. The capability of the proposed method for concealment in diverse image regions is depicted. Our detailed conducted experiments show that the proposed method outperforms the state-of-the-art spatial error concealment methods in... 

In this paper, we present a compressive sampling and multihypothesis (MH) reconstruction strategy for video sequences that has a rather simple encoder, while the decoding system is not that complex. We introduce a convex cost function that incorporates the MH technique with the sparsity constraint and the Tikhonov regularization. Consequently, we derive a new iterative algorithm based on these criteria. This algorithm surpasses its counterparts (Elasticnet and Tikhonov) in recovery performance. Besides, it is computationally much faster than Elasticnet and comparable with Tikhonov. Our extensive simulation results confirm these claims  

We have developed a Gaussian Process Regression method with adaptive kernels for concealment of the missing macro-blocks of block-based video compression schemes in a packet video system. Despite promising results, the proposed algorithm introduces a solid framework for further improvements. In this paper, the problem of estimating lost macro-blocks will be solved by estimating the proper covariance function of the Gaussian process defined over a region around the missing macro-blocks (i.e. its kernel function). In order to preserve block edges, the kernel is constructed adaptively by using the local edge related information. Moreover, we can achieve more improvements by local estimation of... 

Sparse signal processing has found various applications in different research areas where the sparsity of the signal of interest plays a significant role in addressing their ill-posedness. In this invited paper, we give a brief review of a number of such applications in inverse scattering of microwave medical imaging, compressed video sensing, and missing sample recovery based on sparsity. Moreover, some of our recent results on these areas have been reported which confirms the fact that leveraging the sparsity prior of the underlying signal can improve different processing tasks in various problems. © 2016 IEEE  

In recent years, the demand for video transmission over wireless communication networks is growing very fast. The H.264 video compression standard which offers high quality at low bit rates, is a suitable codec for applications that require efficient video transmission over wireless networks. While the compressed videos are transmitted through error-prone networks, error robustness becomes an important issue. In this paper, a joint source-channel Lagrange optimization method in which the distortion of the decoder is estimated without using feedback which can be used for both multicast and point-to-point applications is proposed. The experimental results show that the new algorithm has a good... 

H.264/AVC, the latest video coding standard, achieves better video compression rates since it supports new features such as a large number of intra- and inter-prediction candidate modes. H.264/AVC adopts rate-distortion optimization (RDO) technique to obtain the best intra- and inter-prediction, while maximizing visual quality and minimizing the required bit rate. However, the RDO reduces the encoding speed via the exhaustive evaluation of all candidate modes. In this paper, we decrease the encoding time by reducing the computational complexity of the prediction function and the number of candidate modes. First, we improve Pan's method, by eliminating the DC mode from the candidates. Second,...