Compressed sensing is an acquisition strategy that possesses great potential to accelerate magnetic resonance imaging (MRI) within the ambit of existing hardware, by enforcing sparsity on MR image slices. Compared to traditional reconstruction methods, dictionary learning-based reconstruction algorithms, which locally sparsify image patches, have been found to boost the reconstruction quality. However, due to the learning complexity, they have to be independently employed on successive MR undersampled slices one at a time. This causes them to forfeit prior knowledge of the anatomical structure of the region of interest. An MR reconstruction algorithm is proposed that employs the double sparsity model coupled with online sparse dictionary learning to learn directional features of the region under observation from existing prior knowledge. This is found to enhance the capability of sparsely representing directional features in an MR image and results in better reconstructions. The proposed framework is shown to have superior performance compared to state-of-art MRI reconstruction algorithms under noiseless and noisy conditions for various undersampling percentages and distinct scanning strategies.

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H.2641AVC is the most recent video coding standard developed by the ITU-T Video Coding Experts Group and the ISOREC Moving Picture Experts Group. The main goal ofthe H.264IAVC standard has been to provide enhanced compression performance and provision for network friendly video representation addressing conversational and nonconversational applications. This paper mainly concentrates on two modules of the existing advanced video coding standard. The H. 264 in-loop deblocking filter is one of the several complex techniques that have realized this superior coding quality. The deblocking filter is a computationally and data intensive tool resulting in increased execution time of both the encoding and decoding processes. The existing filter ofthe H.264 video codec is not capable ofsmoothing the video thereby creating an unacceptable blun'ing phenomenon. In this paper, a new deblocking filter has been used to overcome this drawback. It has a non-linear operator that is able to effectively attenuate the noise that cOn'upts the quality of the video. The existing system uses Discrete Cosine Transformation which is proved to have several drawbacks. Hence a hybridization method has been incorporated in transformation module of video encoder. This paper presents an hybridization in the transformation module by incorporating DCT as transformation technique for inter frames and a combination ofwavelet filters for intra frames of video sequence. This proposal is also applied in the recent H.2641A VC standard and is proved to have better results.

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