Example learning and augmented visual quality-based 3D image cutting method

Abstract

The invention relates to an example learning and augmented visual quality-based 3D image cutting method. The method comprises the steps of calculating GIST features and HIST features of images in a professional 3D image library; inputting a to-be-cut image and a target size, and obtaining a candidate cutting window set by adopting a sampling method; calculating the similarity between candidate cutting windows and the GIST and HIST features of the images in the image library, obtaining n images most similar to the candidate cutting windows, and performing combination to form a learnt example set; assessing compositions and depth information distribution quality of the candidate cutting windows as well as information loss and stereoscopic image visual comfort rule following conditions by utilizing examples, and calculating the cutting window of a left view; and through the learnt examples, horizontally moving the cutting window of a right view to obtain depth distribution most similar tothe learnt examples, thereby obtaining a final cutting result. The method is favorable for obtaining the cutting result with the visual comfort, and can be applied to the fields of image processing,computer vision and the like.

Description

technical field [0001] The invention relates to the fields of image processing and computer vision, in particular to a 3D image cropping method based on example learning and visual quality enhancement. Background technique [0002] Image cropping is one of the most basic image processing operations. Since 2003, experts and scholars have continuously studied and proposed image cropping methods based on content awareness, that is, methods based on intelligent cropping. This type of method first crops according to the importance of the image information, initially obtains the cropped area with the same aspect ratio as the target screen, and then adapts the content of the cropped area to the size of the target screen using proportional scaling technology. Liu H. et al. determine the location information of important content in the image through eyetracking, and then cut out the content area containing the least important information in an interactive way. Liu F. et al. first d...

AI Technical Summary

Problems solved by technology

[0004] Unlike cropping for 2D images, cropping for 3D images is a challenging task because the quality of 3D images is highly subjective and the cropping window for 3D images may lead to violation of visual comfort rules for stereoscopic images

The difficulty of the 3D image cropping method is to ensure that the cropped results can be viewed comfortably through a stereoscopic display device

Existing automatic stereoscopic image cropping methods do not consider this problem

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