Abstract
Due to the complex angular-spatial structure, light field (LF) image processing faces more opportunities and challenges than ordinary image processing. The angular-spatial structure loss of LF images can be reflected from their various representations. The angular and spatial information penetrate each other, so it is necessary to extract appropriate features to analyze the angular-spatial structure loss of distorted LF images. In this paper, a LF image quality evaluation model, namely MPFS, is proposed based on the prediction of global angular-spatial distortion of macro-pixels and the evaluation of local angular-spatial quality of the focus stack. Specifically, the angular distortion of the LF image is first evaluated through the luminance and chrominance of macro-pixels. Then, we use the saliency of spatial texture structure to pool an array of predicted values of angular distortion to obtain the predicted value of global distortion. Secondly, the local angular-spatial quality of the LF image is analyzed through the principal components of the focus stack. The focalizing structure damage caused by the angular-spatial distortion is calculated using the features of corner and texture structures. Finally, the global and local angular-spatial quality evaluation models are combined to realize the evaluation of the overall quality of the LF image. Extensive comparative experiments show that the proposed method has high efficiency and precision.
Highlights
Light field (LF) imaging technology is designed to record rich scenario information
Speaking, the lenslet image and epipolar plane images (EPIs) directly reflect the distortion of angular information, while the focus stack and SAIs directly reflect the distortion of spatial information
We propose a new light field (LF) quality evaluation method through the global angular-spatial quality framework based on macro-pixels and the local angular-spatial quality framework based on the focus stack
Summary
Compared with ordinary two-dimensional (2D) images and binocular stereoscopic images, LF images are favored in researches like immersive stereoscopic display and object recognition because of their particular characteristics of dense view and post-focusing (Huang et al, 2016; Ren et al, 2017a). For these applications, image quality degradation will directly affect the perception of the immersive experience and the accuracy of object recognition. The quality assessment of LF images is different from that of ordinary image types It involves analyzing the complex imaging structure relationships among dense multi-view LF images. It is of great significance for the development of LF to build an objective quality evaluation model that effectively utilizes the characteristics of LF images
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