Method for extracting video interested region based on visual attention

Abstract

The invention discloses a method for extracting a video interested region based on a visual attention. The interested region extracted by the method combines a static image domain visual attention, amotion visual attention and a depth perception attention, and the method efficiently restrains the internal unicity and inaccuracy of each visual attention extraction and solves the problem of noisescaused by a complex background in the static image domain visual attention and the problem that the motion visual attention can not extract an interested region with small local motion and motion amplitude, thus the method improves the calculation accuracy, enhances the algorithm stability and extracts an interested region from a background with complex veins and the motion environment; furthermore, the interested region obtained by the method accords with the depth perception feature interesting on an object having a strong depth perception or a short distance in the stereovision and the semantic characteristic of human eye stereovision besides according with the vision feature of human eye interesting a static grain video frame and interesting a motion object.

Description

technical field [0001] The invention relates to a method for processing video signals, in particular to a method for extracting video interest regions based on visual attention. Background technique [0002] Stereoscopic TV, also known as 3DTV (Three Dimensional Television, three-dimensional TV), because stereoscopic TV can provide a leap from flat to three-dimensional, and give viewers a unique sense of three-dimensionality and reality, it has been highly regarded by research institutions and industries at home and abroad. Pay attention to. In 2002, an ATTEST (Advanced 3D Television System Technology) project was launched in the IST program supported by the European Commission. The project aims to establish a complete backward compatible 3D digital TV broadcasting chain system. The goal of the ATTEST project is to propose a new concept of 3DTV broadcasting chain, realize backward compatibility with existing 2D broadcasting, and widely support various forms of 2D and 3D dis...

AI Technical Summary

Problems solved by technology

[0004] However, the current automatic region of interest extraction is mainly divided into three categories, 1) using the internal information of a single viewpoint image, including stimulus information such as brightness, color, texture or direction, to extract the region of interest of the human eye on the current video frame, This method mainly extracts regions with large differences in brightness, color and texture contrast as regions of interest, which makes this method difficult to apply to the region of interest extraction in complex background environments; The principle is to use the motion information between video frames as the main clue to extract the region of interest. However, this method is difficult to accurately extract slow or local moving objects, and it is also difficult to apply to the region of interest extraction in the case of global motion; 3 ), using the extraction method combining static texture and motion information. Due to the weak redundancy and correlation between static texture and motion information, this method cannot effectively suppress the existing extraction errors and noises, so that the extraction accuracy is not good. high

Due to the limitation of the amount of information available, these three types of traditional methods cause the extracted regions of interest to be inaccurate and poor in stability; on the other hand, the traditional methods do not consider being interested in objects with a strong sense of depth or closer to the viewer. Stereo vision characteristics, can not well represent the true degree of interest of the human eye with stereo vision, so it is difficult to apply to the region of interest extraction that conforms to the semantic features of stereo vision in the new generation of stereo (3D) / multi-viewpoint video

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