Background modeling-based static camera video compression method and background modeling-based static camera video compression system

Abstract

The invention discloses a background modeling-based static camera video compression method and a background modeling-based static camera video compression system. The method comprises the following steps: for an input video sequence, selecting a training image set and modeling the selected training image set to generate a background image; adopting a coder to encode and compress the background image to generate a background image code stream; adopting a decoder to decode and reconstruct the background image code stream to acquire a reconstructed background image; differentiating images in the input video sequence and the reconstructed background image, calculating a differentiation result and acquiring a differential image according to the differentiation result; and encoding and compressing the differential image. In the method and the system, a fixed scene part and other parts of video are compressed respectively by utilizing the characteristic that a scene of the video sequence is fixed and establishing and updating a model describing the relatively fixed scene in the video so as to eliminate redundancy in the video sequence to a greater extent to obtain better compression performance.

Description

technical field [0001] The invention relates to video compression technology in the technical field of digital media processing, in particular to a method and system for static camera video compression based on background modeling. Background technique [0002] Video compression (also known as video coding) is one of the key technologies in applications such as digital media storage and transmission. Its purpose is to reduce the amount of data in storage and transmission by eliminating redundant information. All current mainstream video compression standards adopt a block-based predictive transformation hybrid coding framework, which eliminates statistical redundancy (including spatial redundancy, temporal redundancy, and information entropy redundancy) in video images through methods such as prediction, transformation, and entropy coding. surplus), in order to achieve the purpose of reducing the amount of data. At present, most video compression technologies are oriented t...

AI Technical Summary

Problems solved by technology

Object-based video compression methods can be used to compress video captured by static cameras, but there are two problems: first, object detection and segmentation in video is still an unsolved problem in the field of computer vision and image processing, and now Some methods are still not ideal in the accuracy and accuracy of detection and segmentation, which has become a bottleneck in the object-oriented video compression method; second, the computational complexity of the above-mentioned object detection and segmentation methods is high, which is not conducive to the realization of the encoder

However, explicitly performing video foreground / background segmentation faces similar difficulties as object detection and segmentation

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