Method and device for screening intra-frame prediction mode of video frame

Abstract

The invention discloses a method for screening an intra-frame prediction mode of a video frame based on texture analysis. The method comprises steps that the image texture of the video frame is calculated to obtain a gradient vector of each pixel point in the video frame; the gradient vectors of all the pixel points are divided into two parts according to the direction, first mapping in different directions is carried out, second mapping in the same direction is carried out, the gradient vector of each pixel point is converted into a vector, representing the optimal prediction direction, of each pixel point, the directions of vectors representing the optimal prediction direction of all the pixel points are distributed in a 180-degree angle range which is the same as 33 intra-frame prediction modes supported by H.265 or after 45-degree rotation; a prediction vector of each intra-frame prediction block is obtained according to vector superposition of each pixel point representing the optimal prediction direction in the video frame, and an optimal intra-frame prediction mode is obtained through mapping. According to the method, vector rotation and half-plane mapping are carried out on the texture analysis result, and the physical relation between the gradient vector and the prediction direction is better reflected.

Description

technical field [0001] The present application relates to a digital video or image compression encoding method, in particular to a method for screening intra-frame prediction modes of video frames. Background technique [0002] Compression coding of digital video or image is to save storage space or transmission bandwidth by removing spatial redundancy, time redundancy and statistical redundancy in images. [0003] In the field of digital images, still images are usually expressed as a two-dimensional dot matrix, each point represents a pixel, and the number of pixels in the horizontal and vertical directions represents the resolution of the image. The pixel value indicates the brightness and color of the pixel, usually RGB (Red, Green, Blue; red, green, blue), YCbCr (luma component, blue chroma component, red chroma component; brightness component, blue chroma component, red Chroma component), HSV (Hue, Saturation, Value; hue, saturation, lightness) and other different col...

AI Technical Summary

Problems solved by technology

As another example, the gradient vector superposition of pixels is generally performed by simple summation or mathematical average, without considering the end-to-end characteristics of intra prediction modes

Images