Unifying Structural and Semantic Similarities for Quality Assessment of DIBR-Synthesized Views

• Published in: IEEE access Vol. 10; pp. 59026 - 59036
• Main Authors: Mahmoudpour, Saeed, Schelkens, Peter
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolutional neural networks; Deep neural networks; depth image-based rendering; Distortion; Feature extraction; Image quality; image semantics; Measurement; Misalignment; Pixels; Quality assessment; saliency map; Semantics; Similarity; Source code; Synthesis; visual quality assessment; Visualization

Multi-view 3D content is subject to distortions during the process of depth image-based rendering (DIBR). Studies have shown the unreliable performance of the well-established image quality assessment (IQA) models for evaluation of DIBR-synthesized views which surge the need for more effective IQA m...