Abstract
Blind quality assessment of 3D images is used to confront more real challenges than 2D images. In this Letter, we develop a no-reference stereoscopic image quality assessment (SIQA) model based on the proposed left and right (LR)-similarity map and structural degradation. In the proposed method, local binary pattern features are extracted from the cyclopean image that are effective for describing the distortion of 3D images. More importantly, we first propose the LR-similarity map that can indicate the stereopair quality and demonstrate that the use of LR-similarity information results in a consistent improvement in the performance. The massive experimental results on the LIVE 3D and IRCCyN IQA databases demonstrate that the designed model is strongly correlated to subjective quality evaluations and competitive to the state-of-the-art SIQA algorithms.
Highlights
The existing mature 2D algorithms [6] can be directly applied to the left and right views to predict the overall scores of the stereopairs
The cyclopean image model has brought about a multitude of promising 3D-IQA algorithms
Shao et al [10] developed a blind stereoscopic image quality assessment (BSIQA) model based on a binocular feature combination, which led to a more natural and convenient representation of binocular visual perception
Summary
X where x are the pixel values within a block, and p x are the relative frequency density. To explore the behavior of the local spatial entropy values against the LR-similarity maps that are produced from the same images with different degrees and types of distortions, we conducted a series of validation experiments. “JP2K,” “JPEG,” “WN,” and “FF” tend to increase the mean sharply and induce the histogram to be typically “left-skewed.”. Since we believe that there exists a strong relationship between spectral entropy values and the LR-similarity maps under different distortion types and degree, the block discrete cosine transform (DCT) coefficient matrix is computed on each 8 × 8 block. The support vector regression (SVR) is employed as the mapping function from the 2D and 3D feature vectors to the quality score
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