Name
Abstract | ||
|---|---|---|
The paper introduces a novel L-infinity-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-infinity distortion. A fixed-rate L-infinity 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-infinity codec extension is proposed, which enables encoding the quantized residuals in a number of enhancement layers. The experimental results show that the proposed L-infinity coding approach substantially outperforms the L-infinity coding extension of the state-of-the-art CALIC method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01449-0_40 | ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2018 |
Keywords | Field | DocType |
L-infinite norm,Optimized fixed-rate quantization,Depth map compression,Context modeling | Discrete mathematics,Entropy encoding,Pattern recognition,Computer science,Segmentation,Coding (social sciences),Quantization (physics),Artificial intelligence,Geometric distribution,Distortion,Codec,Encoding (memory) | Conference |
Volume | ISSN | Citations |
11182 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenqi Chang | 1 | 0 | 0.34 |
| I. Schiopu | 2 | 37 | 8.04 |
| Adrian Munteanu | 3 | 664 | 80.29 |