For Audio Video coding Standard Ⅱ(AVS2) intra-prediction mode determination process is complicated to calculate, and the popularity of ultra-high definition video put encoding and decoding system under great pressure, a kind of fast intra prediction algorithm was presented in this paper. The algorithm selected the part of the Smallest Coding Unit (SCU) prediction mode,reducing the amount of computation of the underlying SCU, and then the upper layer Coding Unit (CU) obtained the prediction mode by the lower CU prediction mode, thereby reducing the amount of computation of the upper CU. The experimental results show that the impact on the compression efficiency of the algorithm is very small, the encoding time on average decreases more than 15 percent, and can effectively reduce the complexity of intra-coding.

Image registration is a well researched topic of computer vision. To deal with matching efficiency, repetitive pattern matching and affine invariant matching better, two improvements over the state-of-the-art Affine-Scale Invariant Feature Transform (ASIFT) algorithm were presented. The feature extraction of matching frame was developed to improve the matching efficiency of the ASIFT algorithm. The second increased the accuracy of matching and the adaptive capacity of repetitive patterns through the use of improved matching algorithm by combining Optimized Random Sample Consensus (ORSA) with Random Sample Consensus (RANSAC) algorithm based on geometric linear constraint model with homography matrix. The experimental results show that the proposed method is able to well match highly repetitive patterns and has smaller calculation, faster speed and higher accuracy as well.