A Rotate-based Best Neighborhood Matching Algorithm for High Definition Image Error Concealment

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 which it then uses to repair or conceal errors. On a high-definition image BNM requires a relatively long time and so is not suitable for real-time or high volume use. In this paper, a rotate-based BNM algorithm is proposed and implemented which searches the best matching block from the broken block to the outside in a rotating style and dynamically detects and cuts the search range for a damaged block. Experiment results show that our approach can speed up BNM more than twenty times without any obvious loss of accuracy.