Estimating high-resolution (HR) video from a sequence of low-resolution (LR) compressed observations is the focus of this paper. Based on the theory of regularization, this paper proposes a new form of regularized cos...
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Estimating high-resolution (HR) video from a sequence of low-resolution (LR) compressed observations is the focus of this paper. Based on the theory of regularization, this paper proposes a new form of regularized cost function to control the within-channel balance between received data and prior information, and a channel weight coefficient to control the cross-channel fidelity. The LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated for each channel with ameliorating artifacts in compressed video. An iterative gradient descent algorithm is utilized to reconstruction the HR video. Experimental results demonstrate that the proposed algorithm has an improvement in terms of both objective and subjective quality
Super-resolution (SR) technique is to estimate the high-resolution (HR) images by combining the non-redundant information that is available into a set of low-resolution (LR) images, which has been a great focus for co...
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Super-resolution (SR) technique is to estimate the high-resolution (HR) images by combining the non-redundant information that is available into a set of low-resolution (LR) images, which has been a great focus for compressed video. Based on the theory of projection onto convex set (POCS), this paper constructs quantization constraint set (QCS) using the motion between the frames and the quantization information embedded from the video bit stream. By combing the statistical properties of image and the human visual system (HVS), a novel adaptive quantization constraint set (AQCS) is proposed. The proposed algorithm and its performance analysis are also described. Simulation results show that AQCS-based SR algorithm obtains better performance in both objective and subjective quality, which is applicable for compressed video
In this paper, we propose an adaptive fractional pixel search algorithm for reduction computation its load. Based on that SAD (sum of absolute difference) error surface is unimodal within the range of plusmn1 pixel, a...
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In this paper, we propose an adaptive fractional pixel search algorithm for reduction computation its load. Based on that SAD (sum of absolute difference) error surface is unimodal within the range of plusmn1 pixel, a novel fractional pixel search bypass strategy is first proposed; Then, a strategy of fractional pixel search early termination based on all-zero block detection is proposed. Finally, an adaptive fractional pixel search algorithm adopting above strategies and improved CBFPS (center biased fractional pixel search) algorithm is proposed. Compared with the fractional pixel full search algorithm and JVT-F017 that adopts CBFPS, simulation results show that the proposed algorithm can reduce 76.60% and 68.22% fractional pixel search points respectively while it still maintains similar coding efficiency.
In order to resolve the problems of discontented restoration effect and confined applying scope which exist in the current compressed video restoration algorithms, a novel method to get super-resolution images from lo...
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In order to resolve the problems of discontented restoration effect and confined applying scope which exist in the current compressed video restoration algorithms, a novel method to get super-resolution images from low-resolution compressed video is proposed in this paper. At first, a uniform model is presented and the restoration problem in the Bayesian framework is formulated under the MAP criterion, then the focus is put on the hybrid motion-compensation and transform coding schemes, at last the methods of getting the parameters are provided. The results of the simulation clearly demonstrate that our method not only has the properties of finer vision effect and wider applying scope, but also performs better than those of current classical algorithms in the aspects of Peak Signal Noise Ratio （PSNR） under the basis of the same condition.
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