Acceleration of Local Intensity Compensation using sparse representation with parallel processing

The novel motion compensation method, called LIC (Local Intensity Compensation), approximates a current block using a linear combination of reference blocks. To reduce the bitrate of high quality images, sparse coefficients are preferable; then, a method applying sparse representation to the LIC has been proposed. A solver of sparse representation called AIHT (Accelerated Iterative Hard Thresholding) uses many vector and matrix operations, and video coding standards encode frames in GOP (Group Of Pictures) structure. In some cases, video coding methods have to encode videos in real time, but the LIC using sparse representation takes a large amount of computational time for the prediction; thus, this paper demonstrates a method to accelerate the LIC using sparse representation as follows: (i) acceleration of the AIHT itself using GPU (Graphics Processing Unit); and (ii) employing parallel processing in multi-core CPU, namely OpenMP, for the independent structure of the GOP. By using OpenMP, multiple GPUs work under different cores. Experimental results show that the methods (i) and (ii) are faster than CPU implementation by about 4 times and about 7.4 times maximum, respectively.