The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
A local search approximation algorithm for k-means clustering
Proceedings of the eighteenth annual symposium on Computational geometry
A generic framework of user attention model and its application in video summarization
IEEE Transactions on Multimedia
Modeling visual attention's modulatory aftereffects on visual sensitivity and quality evaluation
IEEE Transactions on Image Processing
Optimized Rate-Distortion Extraction With Quality Layers in the Scalable Extension of H.264/AVC
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
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We conduct subjective tests to evaluate the performance of scalable video coding with different spatial-domain bit-allocation methods, visual attention models, and motion feature extractors in the literature. For spatial-domain bit allocation, we use the selective enhancement and quality layer assignment methods. For characterizing visual attention, we use the motion attention model and perceptual quality significant map. For motion features, we adopt motion vectors from hierarchical B-picture coding and optical flow. Experimental results show that a more accurate visual attention model leads to better perceptual quality. In cooperation with a visual attention model, the selective enhancement method, compared to the quality layer assignment, achieves better subjective quality when an ROI has enough bit allocation and its texture is not complex. The quality layer assignment method is suitable for region-wise quality enhancement due to its framebased allocation nature.