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This paper proposes some extensions of the successful sparse coding of still images to intraframe and semi-intraframe video coding. The presented frameworks apply the efficient K-singular value decomposition and recursive least squares dictionary learning methods for sparse representation of videos to study their coding performances. In the proposed semi-intraframe schemes, namely, SISC1 and SISC2, only frame-blocks with more than a threshold deviation from the blocks of the previous frame are transmitted/coded. This reduces the required bitrate and prevents the sparse coding of similar blocks, leading to more efficient video coding methods. The results show that the dictionary learning-based intraframe coding improves the rate-distortion performance of the conventional Motion-JPEG and Motion-JPEG2000 at low bitrates for more than about 3 and 0.5 dB of PSNR on average (for 0.2-1 bpp compression), respectively. The proposed methods outperform the basic dictionary learning-based coding, especially for slower changing videos, generally, with more than 3 dB superiority on average over the tested bitrates. These schemes even present superior performance than the HEVC in the intramode for the complex textured or cluttered scenes. The proposed SISC2 method also saves up to about 50% of the sparse coding computational cost by preventing the coding of more similar frame-blocks. 1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Background Susceptibility-weighted imaging (SWI) is efficient in detecting multiple sclerosis (MS) plaques and evaluating the level of disease activity. Purpose To automatically detect active and inactive MS plaques in SWI images using a Bayesian approach. Material and Methods A 1.5-T scanner was used to evaluate 147 patients with MS. The area of the plaques along with their active or inactive status were automatically identified using a Bayesian approach. Plaques were given an orange color if they were active and a blue color if they were inactive, based on the preset signal intensity. Results Experimental findings show that the proposed method has a high accuracy rate of 91% and a sensitivity rate of 76% for identifying the type and area of plaques. Inactive plaques were properly identified in 87% of cases, and active plaques in 76% of cases. The Kappa analysis revealed an 80% agreement between expert diagnoses based on contrast-enhanced and FLAIR images and Bayesian inferences in SWI. Conclusion The results of our study demonstrated that the proposed method has good accuracy for identifying the MS plaque area as well as for identifying the types of active or inactive plaques in SWI. Therefore, it might be helpful to use the proposed method as a supplemental tool to accelerate the specialist's diagnosis.