Abstract:
The paper presents a comprehensive software system for automatically evaluating stereoscopic distortions in VR180 video. The proposed approach takes into account the most common types of artifacts: color mismatches, differences in sharpness, geometric distortions (vertical shift, rotation, scaling), and channel mismatches. Specialized algorithms have been developed for each type of distortion, based on disparity maps, motion vectors and their confidence maps, as well as neural network regression or classification methods. The proposed solutions have been successfully tested on several datasets, demonstrating high accuracy in detecting various types of distortions in VR180 video. The system can be integrated into standard post-processing pipelines and provides automated generation of detailed reports, allowing stereoscopic content creators to quickly identify and eliminate stereoscopic distortions before releasing their products to a wide audience.
Keywords:stereoscopic distortions, stereoscopic video, VR180, color mismatch, sharpness mismatch, geometric distortions, channel mismatch, deep learning.
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