Motion-compensated frame interpolation (MCFI) is a technique used extensively for increasing the temporal frequency of a video sequence. In order to obtain a high quality interpolation, the motion field between frames must be well-estimated. However, many current techniques for determining the motion are prone to errors in occlusion regions, as well as regions with repetitive structure. We propose an algorithm for improving both the objective and subjective quality of MCFI by refining the motion vector field. We first utilize a discriminant saliency classifier to determine which regions of the motion field are most important to a human observer. These regions are refined using a multistage motion vector refinement (MVR), which promotes motion vector candidates based upon their likelihood given a local neighborhood. For regions which fall below the saliency-threshold, a frame segmentation is used to locate regions of homogeneous color and texture via normalized cuts. Motion vectors are promoted such that each homogeneous region has a consistent motion. Experimental results demonstrate an improvement over previous frame rate up-conversion (FRUC) methods in both objective and subjective picture quality.

We propose a novel online learning-based framework for occlusion boundary detection in video sequences. This approach does not require any prior training and instead "learns" occlusion boundaries by updating a set of weights for the online learning Hedge algorithm at each frame instance. Whereas previous training-based methods perform well only on data similar to the trained examples, the proposed method is well suited for any video sequence. We demonstrate the performance of the proposed detector both for the CMU data set, which includes hand-labeled occlusion boundaries, and for a novel video sequence. In addition to occlusion boundary detection, the proposed algorithm is capable of classifying occlusion boundaries by angle and by whether the occluding object is covering or uncovering the background.

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High-dynamic range displays provide impressive image quality, but require markedly higher bandwidth. Here we report results of the first large-scale subjective assessment of HDR image compression. We applied the ISO/IEC 29170-2 flicker paradigm to evaluate two state-of-the art VESA codecs (DSC v1.2a, pre-release version of VDC-M) at several compression levels.

A new method for scale-aware saliency detection is introduced in this work. Scale determination is realized through a fast scale-space algorithm using color and texture. Scale information is fed back to a Discriminant Saliency engine by automatically tuning centersurround parameters. Excellent results are demonstrated for predicted fixations using a public database of measured human fixations. Further evidence of the proposed algorithm's performance is exhibited through an application to Frame Rate Up-Conversion (FRUC). The ability of the algorithm to detect salient objects at multiple scales allows for class-leading performance both objectively in terms of PSNR/SSIM as well as subjectively. Finally, the need for operator tuning of saliency parameters is dramatically reduced by the inclusion of scale information. The proposed method is well-suited for any application requiring automatic saliency determination for images or video.