Multi-mode lossless compression implementation method for human skeleton in video

Abstract

The invention discloses a multi-mode lossless compression implementation method for a human skeleton in a video. The multi-mode lossless compression implementation method comprises the steps of: selecting key frames, and distinguishing whether the human skeleton in each frame in the video is a changed human skeleton or not; then subjecting the changed human skeletons to special marking and labeling; and carrying out selective coding according to whether each frame of image is a key frame or not and whether each skeleton in the frame is a changed human skeleton or not. Through organic cooperation and combination of the three encoding methods, lossless compression encoding is carried out on the human skeleton information in the video sequence, so that a small-volume encoding bit stream for transmission is obtained, and the compression efficiency of the human skeleton information is remarkably improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows for efficient compressing data representing human body parts into smaller files that are easier to transmit over communication networks or storage devices without losing important details such as their shape or position within them. By comparing these compressed images against existing databases containing similar shapes/sizes, they may find areas where there're specific movements between certain bones (or other features). These movement patterns help identify which area needs further analysis based upon its location inside this image. Overall, this new technique improves efficiency and reduces costs compared to previous methods like raster scanning techniques.

Problems solved by technology

This patented technical problem addressed in this patents relates to finding ways to compress data from cameras capturing human body parts into smaller sizes while maintaining their original appearance when transmitted over networks or storage devices like hard drives (HDD). Current methods are limited due to complexity issues with man-made objects that can change shape during playback making it challenging to accurately represent them correctly without losing any significant amount of image quality.

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