A Reference-Free Framework for Stereoscopic Image Quality Evaluation Using Wavelet and Sharpness Features of Scene Components

Abstract

Stereoscopic-3D (S3D) technology has gained widespread adoption recently across multimedia-based consumer applications due to its ability to develop immersive perceptual experiences. With this tremendous utilization, assessing the generated S3D content quality is essential for achieving consistent and reliable performance across diverse multimedia systems and devices. To automate this process, we develop a reference-free image quality assessment (IQA) algorithm based on performing a multiscale biorthogonal wavelet transform on the cohesive color map derived from an S3D image. We model the resulting sub-bands using a univariate generalized Gaussian distribution and estimate the corresponding fitting coefficients. We demonstrate that the computed features effectively distinguish distortions and estimate the Chi-square distance between test image features and pristine model parameters to measure the primitive chrominance feature of an S3D image. Further, we perform a second derivative Laplacian operator on the cohesive color map to measure the overall sharpness of chromatic information of an S3D scene. Next, we compute the perceptual image quality evaluator on the left and right scenes to estimate the overall luminance feature of an S3D image. Finally, chromatic and luminance-based features are linearly combined to calculate the overall quality score of an S3D image. We evaluate our model on four benchmark datasets (LIVE Phase I & II, Waterloo Phase I & II), demonstrating its superior performance against eighteen existing IQA models, including 2D and 3D opinion-unaware and aware algorithms. This algorithm provides a highly effective solution to ensure the data quality required for the next generation of multimedia applications. The source code for the proposed opinion-unaware model can be accessed via the following: Google Drive link. � 1975-2011 IEEE.