HEVC Compression Distortion Estimation in Depth Maps

No-Reference Quality Metric for HEVC Compression Distortion Estimation in Depth Maps

Muhammad Shahid Farid¹, Maurizio Lucenteforte² and Marco Grangetto²

¹Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan.
²Dipartimento di Informatica, Universittà degli Studi di Torino, Torino, Italy.

Abstract: Multiview video plus depth (MVD) is the most popular 3D video format due to its efficient compression and provision for novel view generation enabling the free-viewpoint applications. In addition to color images, MVD format provides depth maps which are exploited to generate intermediate virtual views using the depth image based rendering (DIBR) techniques. Compression affects the quality of the depth maps which in turn may introduce various structural and textural distortions in the DIBR synthesized images. Estimation of the compression related distortion in depth maps is very important for a high quality 3D experience. The task becomes challenging when the corresponding reference depth maps are unavailable e.g., when evaluating the quality on the decoder side. In this paper, we present a no-reference quality assessment algorithm to estimate the distortion in the depth maps induced by compression. The proposed algorithm exploits the depth saliency and local statistical characteristics of the depth maps to predict the compression distortion. The proposed 'depth distortion evaluator' (DDE) is evaluated on depth videos from standard MVD database compressed with the state-of-the-art high efficiency video coding (HEVC) at various quality levels. The results demonstrate that DDE can be used to effectively estimate the compression distortion in depth videos.

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• Matlab Implementation of DDE
• Function used for Nonlinear Regression in experimental evaluation of DDE

Table 1. Performance comparison in terms of Pearson linear correlation coefficient (PLCC). The values in bold represents the best performing metric.

Table 2. Performance comparison in terms of Root Mean Square Error (RMSE). The values in bold represents the best performing metric.

Last updated: April 30, 2019