- Title
- A novel depth edge prioritization based coding technique to boost-UP HEVC performance
- Creator
- Podder, Pallab; Paul, Manoranjan; Murshed, Manzur
- Date
- 2016
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161025
- Identifier
- vital:12380
- Identifier
-
https://doi.org/10.1109/ICMEW.2016.7574734
- Abstract
- In addition to the texture, multiview video employs the utilization of depth coding for the reconstruction of 3D video and Free viewpoint video. Standing on some texture-depth correlations, a number of methods in literature reuses texture motion vector for the corresponding depth coding to reduce encoding time by avoiding costly motion estimation process. However, texture similarity metric is not always equivalent to the corresponding depth similarity metric especially at edge levels. Since their approaches could not explicitly detect and encode acute edge motions of depth objects, eventually, could not reach the similar or improved rate-distortion (RD) performance against the High Efficiency Video Coding (HEVC) reference test model (HM). With a view to more accurate motion detection and modeling, the proposed technique exploits an extra Pattern Mode comprising a group of pattern templates (GPTs) with different rectangular and non-rectangular object shapes and edges compared to the existing HEVC block partitioning modes. Moreover, the proposed Pattern Mode only encodes the motion areas and skips the background areas. The experimental results show that the proposed technique could save 30% encoding time and improve average 0.1dB Bjontegard Delta peak signal-to-noise ratio (BD-PSNR) compared to the HM.
- Publisher
- IEEE
- Relation
- 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
- Rights
- © 2015 IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; HEVC; Depth edge; Motion estimation; Pattern mode; Intermode selection
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