No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement

Abstract

Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.Index Terms-Light field image, quality assessment, epipolar plane image, naturalness, spatial quality, angular consistency.

Light Field Image Super-Resolution Using Deformable Convolution

“…Following [92], we use four no-reference image quality assessment (NRIQA) metrics (i.e, BRISQUE [64], NIQE [65], CEIQ [66], ENIQA [67]) to evaluate the perceptual quality of the center-view images of these datasets. Besides, we also use a no-reference LF quality assessment metric (i.e., NRLFQA [68]) to evaluate TABLE I: Main characteristics of several popular LF datasets. Note that, average scores are reported for spatial resolution (SpaRes), single-image perceptual quality metrics (i.e., BRISQUE [64], NIQE [65], CEIQ [66], ENIQA [67]) and LF quality assessment metrics (i.e., NRLFQA [68]).…”

“…Besides, we also use a no-reference LF quality assessment metric (i.e., NRLFQA [68]) to evaluate TABLE I: Main characteristics of several popular LF datasets. Note that, average scores are reported for spatial resolution (SpaRes), single-image perceptual quality metrics (i.e., BRISQUE [64], NIQE [65], CEIQ [66], ENIQA [67]) and LF quality assessment metrics (i.e., NRLFQA [68]). the spatial quality and angular consistency of LFs.…”

No-Reference Deep Quality Assessment of Compressed Light Field Images

“…The proposed method is compared with the previous 2D image quality assessment methods including in FR metrics (PSNR, SSIM [4], MS-SSIM [5], FSIM [27], and VIF [28]) and NR metrics (BRISQUE [7] and NIQE [8]) and LF image quality assessment method ( NR-LFQA [14] ).…”

“…Besides, several no-reference LF image quality assessment (NR-LFIQA) methods had also been proposed. Shi et al propose three NR-LFIQA models, including BELIF [13], NR-LFQA [14], and Tensor-NLFQ [15]. BELIF [13] uses binocular vision features of LF images to predict the quality.…”

“…BELIF [13] uses binocular vision features of LF images to predict the quality. NR-LFQA [14] extracts naturalness of LF cyclopean image to measure spatial quality, calculates the distribution of gradient direction map of Epipolar Plane Image (EPI) to measure angular consistency distortion, and uses weighted local binary to capture local angular consistency degradation feature. Based on BELIF, Tensor-NLFQ [15] exploits angular consistency of each direction and takes the influence of luminance and chrominance on perceptual quality into consideration.…”