No abstract

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.

Unlike familiar macroblock-based in-loop deblocking filter in H.264, the filters of VC-1 perform all horizontal edges (for in-loop deblocking filtering) or vertical edges (for overlap smoothing) first and then the other directional filtering edges. The entire procedure is very time-consuming and with high memory access loading for the whole system. This paper presents a novel method and the efficient integrated architecture design, which involves an 12×12 overlapped block that combines overlap smoothing with loop filtering for performance and cost by sharing circuits and resources. This architecture has capability to process HDTV1080p 30fps video and HDTV 2048×1536 24fps video at 180MHz. The same concept is applicable to other video processing algorithms, especially in deblocking filter for video post-processing in a frame-based order.

Processing (ICIP), Nov. 2009; "Novel Method and Architecture Design for Motion Compensated Frame Interpolation in High-Definition Video Processing," Yen-Lin Lee and Truong Nguyen, revised to IEEE Trans. on Circuits and Systems for Video Technology, 2009. The dissertation author was the primary author of these publications, and the listed co-author directed and supervised the research that forms the basis for this chapter.

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. An algorithm is proposed for improving both the objective and subjective quality of MCFI by refining the motion vector field. A Discriminant Saliency classifier is employed to determine regions of the motion field which are most important to a human observer. These regions are refined using a multi-stage motion vector refinement which promotes candidates based on their likelihood given a local neighborhood. For regions which fall below the saliency threshold, 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 methods in both objective and subjective picture quality.