A low-complexity approach for motion-compensated video frame rate up-conversion
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Video frame rate up-conversion is an important issue for multimedia systems in achieving better video quality and motion portrayal. Motion-compensated methods offer better quality interpolated frames since the interpolation is performed along the motion trajectory. In addition, computational complexity, regularity, and memory bandwidth are important for a real-time implementation. Motion-compensated frame rate up-conversion (MC-FRC) is composed of two main parts: motion estimation (ME) and motion-compensated frame interpolation (MCFI). Since ME is an essential part of MC-FRC, a new fast motion estimation (FME) algorithm capable of producing sub-sample motion vectors at low computational-complexity has been developed. Unlike existing FME algorithms, the developed algorithm considers the low complexity sub-sample accuracy in designing the search pattern for FME. The developed FME algorithm is designed in such a way that the block distortion measure (BDM) is modeled as a parametric surface in the vicinity of the integer-sample motion vector; this modeling enables low computational-complexity sub-sample motion estimation without pixel interpolation. MC-FRC needs more accurate motion trajectories for better video quality; hence, a novel true-motion estimation (TME) algorithm targeting to track the projected object motion has been developed for video processing applications, such as motion-compensated frame interpolation (MCFI), deinterlacing, and denoising. Developed TME algorithm considers not only the computational complexity and regularity but also memory bandwidth. TME is obtained by imposing implicit and explicit smoothness constraints on block matching algorithm (BMA). In addition, it employs a novel adaptive clustering algorithm to keep the low-complexity at reasonable levels yet enable exploiting more spatiotemporal neighbors. To produce better quality interpolated frames, dense motion field at the interpolation instants are obtained for both forward and backward motion vectors (MVs); then, bidirectional motion compensation using forward and backward MVs is applied by mixing both elegantly.
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