As the next generation standard of video coding, high efficiency video coding (HEVC) achieves high coding efficiency. The coding unit (CU) was adopted as the processing unit for HEVC to improve the coding efficiency. However, determining the optimal distribution of CU sizes requires lengthy calculation. To reduce the calculation time in intra prediction processing, we developed a method that determines the CU sizes using the variance value of the input image. Experimental results show that the proposed method reduces encoding time by about 40-70% compared to that of a conventional HEVC test model.

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In this paper, we propose the improved seam merging method for content-aware image resizing. This method merges a twopixel-width seam element into one new pixel in image reduction and inserts a new pixel between the two pixels in image enlargement. To preserve important contents and structure, our method uses importance and structure energies. Using the cartoon image of an original image for calculation of structure energy, our method can preserve main structure. In addition, we introduce new energy to suppress distortion generated by excessive reduction or enlargement in the iterative merging or inserting. Experimental results demonstrate that the proposed method can produce satisfactory results in both image reduction and enlargement.

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SUMMARYDepth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.