The paper introduces a novel L ∞ -constrained compression method for depth maps. The proposed method performs depth segmentation and depth prediction in each segment, encoding the resulting information as a base layer. The depth residuals are modeled using a Two-Sided Geometric Distribution, and distortion and entropy models for the quantized residuals are derived based on such distributions. A set of optimal quantizers is determined to ensure a fix rate budget at a minimum L ∞ distortion. A fixed-rate L ∞ codec design performing context-based entropy coding of the quantized residuals is proposed, which is able to efficiently meet user constraints on rate or distortion. Additionally, a scalable L ∞ codec extension is proposed, which enables encoding the quantized residuals in a number of enhancement layers. The experimental results show that the proposed L ∞ coding approach substantially outperforms the L ∞ coding extension of the state-of-the-art CALIC method.
Chang, W, Schiopu, I & Munteanu, A 2018, L-Infinite Predictive Coding of Depth. in J Blanc-Talon, D Popescu, W Philips, D Helbert & P Scheunders (eds), Advanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Proceedings. 1 edn, vol. 11182, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11182 LNCS, Springer, Poitiers, France, pp. 475-486, International Conference on Advanced Concepts for Intelligent Vision Systems, Poitiers, France, 24/09/18. https://doi.org/10.1007/978-3-030-01449-0_40
Chang, W., Schiopu, I., & Munteanu, A. (2018). L-Infinite Predictive Coding of Depth. In J. Blanc-Talon, D. Popescu, W. Philips, D. Helbert, & P. Scheunders (Eds.), Advanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Proceedings (1 ed., Vol. 11182, pp. 475-486). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11182 LNCS). Springer. https://doi.org/10.1007/978-3-030-01449-0_40
@inproceedings{876ac8f82fb749c88ccd4c8ffe35bd11,
title = "L-Infinite Predictive Coding of Depth",
abstract = "The paper introduces a novel L ∞ -constrained compression method for depth maps. The proposed method performs depth segmentation and depth prediction in each segment, encoding the resulting information as a base layer. The depth residuals are modeled using a Two-Sided Geometric Distribution, and distortion and entropy models for the quantized residuals are derived based on such distributions. A set of optimal quantizers is determined to ensure a fix rate budget at a minimum L ∞ distortion. A fixed-rate L ∞ codec design performing context-based entropy coding of the quantized residuals is proposed, which is able to efficiently meet user constraints on rate or distortion. Additionally, a scalable L ∞ codec extension is proposed, which enables encoding the quantized residuals in a number of enhancement layers. The experimental results show that the proposed L ∞ coding approach substantially outperforms the L ∞ coding extension of the state-of-the-art CALIC method. ",
keywords = "Context modeling, Depth map compression, L-infinite norm, Optimized fixed-rate quantization",
author = "Wenqi Chang and Ionut Schiopu and Adrian Munteanu",
year = "2018",
month = sep,
day = "27",
doi = "10.1007/978-3-030-01449-0_40",
language = "English",
isbn = "978-3-030-01448-3",
volume = "11182",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "475--486",
editor = "Jacques Blanc-Talon and Dan Popescu and Wilfried Philips and David Helbert and Paul Scheunders",
booktitle = "Advanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Proceedings",
edition = "1",
note = "International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2018 ; Conference date: 24-09-2018 Through 27-09-2018",
url = "http://acivs.org/acivs2018/",
}