Effectiveness of 3VQM in Capturing Depth Inconsistencies
This work addresses quality assessment for depth-based 3D videos, which is incremental as it builds on a previously proposed metric.
The paper evaluated the 3VQM metric for detecting depth inconsistencies in 3D videos, finding it best captures errors in reference views but is insensitive to mild depth map errors like blur, and showed it outperforms PSNR, weighted PSNR, and SSIM in accuracy, coherency, and consistency.
The 3D video quality metric (3VQM) was proposed to evaluate the temporal and spatial variation of the depth errors for the depth values that would lead to inconsistencies between left and right views, fast changing disparities, and geometric distortions. Previously, we evaluated 3VQM against subjective scores. In this paper, we show the effectiveness of 3VQM in capturing errors and inconsistencies that exist in the rendered depth-based 3D videos. We further investigate how 3VQM could measure excessive disparities, fast changing disparities, geometric distortions, temporal flickering and/or spatial noise in the form of depth cues inconsistency. Results show that 3VQM best captures the depth inconsistencies based on errors in the reference views. However, the metric is not sensitive to depth map mild errors such as those resulting from blur. We also performed a subjective quality test and showed that 3VQM performs better than PSNR, weighted PSNR and SSIM in terms of accuracy, coherency and consistency.