A fast judgment method of hevc code size based on texture complexity

Abstract

The invention relates to an HEVC coding size rapid determining method based on texture complexity. According to the method, sub-sampling at the ratio of 16:1 is conducted on LCUs, subsequent time spent in calculating the texture complexity is shortened to 1 / 16 of original time, and then a two-stage determining mechanism is introduced; firstly, an ASAD model is used for preliminarily determining the texture complexity of blocks obtained after sub-sampling is conducted on the LCUs, erroneous judgment is prevented by setting two threshold values, and large-size coding units are adopted for the LCUs with simple texture, and small-size coding units are adopted for the LCU with complex texture; secondly, based on upper-stage determination results, an MAD model is adopted, whether coding units in other sizes are added or not is determined, and the accuracy of texture determining is improved. By means of the HEVC coding size rapid determining method based on the texture complexity, the algorithm complexity can be reduced greatly, the texture can be accurately determined, and the algorithm accuracy is improved.

Description

technical field [0001] The invention relates to a method for quickly determining the encoding size for HEVC intra-frame encoding, in particular to a method for quickly determining the encoding size of HEVC based on texture complexity. Background technique [0002] HEVC is a new generation of video coding standard. It was proposed to meet people's new requirements for high-definition, ultra-high-definition, 3D and mobile wireless communications. It officially became an international standard in January 2013. Compared with the widely used previous generation video coding standard H.264, HEVC's intra prediction adopts a large-size quadtree recursive coding structure and up to 35 prediction modes, which greatly increases the accuracy of intra prediction. It can significantly save the number of coding bits and improve the quality of image reconstruction, but the adoption of these technologies greatly increases the complexity of intra-frame prediction calculations. Taking the lar...

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Problems solved by technology

[0003] Aiming at this improvement perspective, many scholars have conducted research. The most commonly used methods for detecting image texture complexity are histogram method, sum of absolute difference method (SAD), mean absolute difference method (MAD), and DC-AC coefficient ratio method. And entropy value method, etc., these methods can characterize the texture complexity of the image to a certain extent, but for the characteristics of HEVC large-size coding blocks, the computational complexity is still high, and these methods reflect the statistical characteristics of pixels , but cannot reflect the change of pixels, and it is not accurate enough in the case of the same statistical characteristics and different textures

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