Images synthesized using depth-image-based-rendering (DIBR) techniques may suffer from complex structural distortions. The goal of the primary visual cortex and other parts of brain is to reduce redundancies of input visual signal in order to discover the intrinsic image structure, and thus create sparse image representation. Human visual system (HVS) treats images on several scales and several levels of resolution when perceiving the visual scene. With an attempt to emulate the properties of HVS, we have designed the no-reference model for the quality assessment of DIBR-synthesized views. To extract a higher-order structure of high curvature which corresponds to distortion of shapes to which the HVS is highly sensitive, we define a morphological oriented Difference of Closings (DoC) operator and use it at multiple scales and resolutions. DoC operator nonlinearly removes redundancies and extracts fine grained details, texture of an image local structure and contrast to which HVS is highly sensitive. We introduce a new feature based on sparsity of DoC band. To extract perceptually important low-order structural information (edges), we use the non-oriented Difference of Gaussians (DoG) operator at different scales and resolutions. Measure of sparsity is calculated for DoG bands to get scalar features. To model the relationship between the extracted features and subjective scores, the general regression neural network (GRNN) is used. Quality predictions by the proposed DoC-DoG-GRNN model show higher compatibility with perceptual quality scores in comparison to the tested state-of-the-art metrics when evaluated on four benchmark datasets with synthesized views, IRCCyN/IVC image/video dataset, MCL-3D stereoscopic image dataset and IST image dataset.

Free-viewpoint video, as the development direction of the next-generation video technologies, uses the depth-image-based rendering (DIBR) technique for the synthesis of video sequences at viewpoints, where real captured videos are missing. As reference videos at multiple viewpoints are not available, a blind reliable real-time quality metric of the synthesized video is needed. Although no-reference quality metrics dedicated to synthesized views successfully evaluate synthesized images, they are not that effective when evaluating synthesized video due to additional temporal flicker distortion typical only for video. In this paper, a new fast no-reference quality metric of synthesized video with synthesis distortions is proposed. It is guided by the fact that the DIBR-synthesized images are characterized by increased high frequency content. The metric is designed under the assumption that the perceived quality of DIBR-synthesized video can be estimated by quantifying the selected areas in the high-high wavelet subband. The threshold is used to select the most important distortion sensitive regions. The proposed No-Reference Morphological Wavelet with Threshold (NR_MWT) metric is computationally extremely efficient, comparable to PSNR, as the morphological wavelet transformation uses very short filters and only integer arithmetic. It is completely blind, without using machine learning techniques. Tested on the publicly available dataset of synthesized video sequences characterized by synthesis distortions, the metric achieves better performances and higher computational efficiency than the state-of-the-art metrics dedicated to DIBR-synthesized images and videos.

In this paper a no-reference image quality assessment (IQA) metric for DIBR-synthesized images is proposed. Sparsity based features of morphologically decomposed image subbands are used to estimate distortion level in images. A General regression neural network is utilized to calculate quality score. The performance is evaluated using publicly available IRCCyN/IVC DIBR image database. Experimental results show that proposed metric accords with human subjective judgment.

In this paper, two image quality metrics based on morphological multiscale decompositions have been applied for the evaluation of free viewpoint video sequences. These sequences are synthesized using decompressed depth maps in the Depth-Image-Based Rendering synthesis process. Since the synthesis introduces edge distortion in the synthesized image/video, morphological filters are used for their ability to maintain important geometric information (i.e., edges) across different resolution levels. The edges displacement in different resolution scales is evaluated by means of the Mean Square Error. The adopted metrics show higher correlation with human judgment than state-of-the-art image quality measures used in this context.