Self-adaptive CU splitting decision-making method based on deep learning and multi-feature fusion

The invention provides an adaptive CU splitting decision method based on deep learning and multi-feature fusion. The method comprises the steps: firstly, calculating the texture complexity SD of a current CU through standard deviation, building a threshold model through a quantization parameter function and a depth function, and dividing the current CU into a complex CU and a uniform CU; secondly,if the complex CU belongs to the edge CU, judging whether the complex CU is split or not by utilizing a CNN structure based on multi-feature fusion; otherwise, judging whether the complex CU is splitor not by utilizing the self-adaptive CNN structure. According to the method, deep learning and multi-feature fusion are combined, and the problem of coding complexity is solved. The CNN structure based on multi-feature fusion and the CNN structure based on self-adaptation can successfully process the training samples, calculation of rate distortion RDO of all CU and complex CU is avoided, and therefore the calculation co