SPEEDING UP BEST NEIGHBORHOOD MATCHING ALGORITHM FOR HIGH-DEFINITION IMAGE ON GPU PLATFORM

Error concealment restores the visual integrity of image content that has been damaged due to a bad network transmission. Best neighborhood matching (BNM) is an effective image-recovery method that exploits the information redundancy in a block-coded broken image to find similar content that it then uses to repair or conceal errors. On a high-definition image, BNM is traditionally implemented sequentially, which requires a relatively long time and thus is not suitable for real-time or high-volume use. In this paper, we analyze the data access patterns of the BNM algorithm, and exploit a graphics process unit (GPU) platform to speed up the execution through a parallel implementation. We compare and combine several different GPU optimization methods (coalesced global memory access, shared memory, register files, etc.), and propose an improvement to the parallel BNM algorithm. Experimental results show that our approach can speed up BNM 62 times over the sequential approach without any obvious loss of accuracy.