Image processing method and device containing motion object, and electronic equipment

Abstract

The embodiment of the invention discloses an image processing method and device containing motion object, and electronic equipment, and relates to the technical field of image processing. The method comprises the following steps that: utilizing depth information corresponding to a current image which contains the motion object to extract the foreground feature point and the background feature point of the current image; on the basis of the inertia data and the background feature point of the current image, determining a background motion vector; obtaining a historical frame image pyramid related to the current image pyramid of the current image and the current image, and determining a foreground motion vector on the basis of the current image pyramid, the historical frame image pyramid andthe foreground feature point; and on the basis of the background motion vector and the foreground motion vector, calculating the motion information of the motion target in the current image, and forming a target image related to the current image. Through the above scheme, system resources can be saved, and image processing efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to the image processing technology including moving objects. Background technique [0002] Game rendering requires a lot of 3D model modeling, rasterization, texture mapping, rendering light and shadow effects, etc. The rendering complexity is very high, so it often causes frame rate bottlenecks. [0003] The prior art mainly aims at the complete video sequence, calculates the non-key frame motion vector off-line, and realizes the interpolation between frames. Such methods have poor real-time performance and complex algorithms, and have not been optimized for mobile platforms. In addition, the existing technology mainly uses color information to analyze images, and does not fuse depth data to reduce redundant calculations and improve efficiency; at the same time, it does not fuse depth data, and adopts multi-scale image processing methods for different depths. Finally, th...

AI Technical Summary

Problems solved by technology

Such methods have poor real-time performance and complex algorithms, and have not been optimized for mobile platforms

In addition, the existing technology mainly uses color information to analyze images, and does not fuse depth data to reduce redundant calculations and improve efficiency; at the same time, it does not fuse depth data, and uses multi-scale image processing methods for different depths

Finally, the existing technology mainly focuses on the accuracy and rapidity of optical flow calculation in color space, and does not propose the fusion method of depth data and the application in image interpolation

A large number of repetitive textures in color space and violation of optical flow assumptions will have a great impact on motion prediction

The existing technology is mainly aimed at the offline rendering of complete video data, or the online prediction of monocular video data, and does not involve the fusion of multi-objective and multi-dimensional information

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