Dynamic 3D point cloud compression fast CU division method, equipment and storage medium

Abstract

This application discloses a dynamic 3D point cloud compression fast CU division method, device and storage medium, including: judging whether the current dynamic 3D point cloud compression coding frame is a P frame; if so, sequentially extracting the target coding CU that needs compression coding, Obtain and transform the prediction distortion of the target coded CU; when the width and height of the target coded CU are 64, and meet the first preset condition; or, when the width and height of the target coded CU are 32, and meet the second preset condition ; or, when the width and height of the target coded CU are 16 and the third preset condition is met, stop further dividing the target coded CU. The above method can predict in advance and accurately whether the target coded CU in the CTU is the optimal code, so that the cumbersome steps of the recursive partition method can be omitted or skipped, thereby ensuring the coding quality of the target coded CU, and significantly reducing the cost of partitioning the target coded CU. Computational complexity in the process.

Description

technical field [0001] The present invention relates to the technical field of point cloud compression, in particular to a fast CU division method, device and storage medium for dynamic 3D point cloud compression. Background technique [0002] The 3D point cloud uses the geometric information, color information, surface normal and other features in the real physical scene to reconstruct a realistic digital 3D scene, providing people with a visual experience from any angle. As a data form that can represent three-dimensional scenes, 3D point cloud has been gradually applied in the fields of virtual reality (Virtual Reality, VR), smart city, robotics and autonomous driving. 3D point cloud mainly includes static point cloud and dynamic point cloud. A dynamic 3D point cloud captured at a rate of 30 frames per second usually has 700,000 points per frame, and the transmission of uncompressed dynamic 3D point cloud data requires a network transmission rate of up to 500MB / s. Obvio...

AI Technical Summary

Problems solved by technology

However, although this exhaustive division strategy can improve the efficiency of compression coding, it also brings huge computational complexity of compression coding, which seriously hinders the real-time transmission of dynamic 3D point clouds.

In addition, since the geometry and color video generated by dynamic 3D point cloud mapping are very different from natural video, this will cause the proposed HEVC fast CU partition method for natural video to not work well for video-based dynamic 3D point Cloud compression coding optimization

At present, there is no effective solution to this problem

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