| Paper: | WP-L3.6 |
| Session: | Video Coding III |
| Time: | Wednesday, September 19, 16:30 - 16:50 |
| Presentation: |
Lecture
|
| Title: |
GEOMETRY-ADAPTIVE BLOCK PARTITIONING FOR INTRA PREDICTION IN IMAGE & VIDEO CODING |
| Authors: |
Congxia Dai; West Virginia University | | |
| | Oscar Divorra Escoda; Thomson | | |
| | Peng Yin; Thomson | | |
| | Xin Li; West Virginia University | | |
| | Cristina Gomila; Thomson | | |
| Abstract: |
Many modern video coding strategies, such as the H.264/AVC standard, use quadtree-based partition structures for coding intra macroblocks. Such a structure allows the coding algorithm to adapt to the complicated and non-stationary nature of natural images. Despite the adaptation flexibility of quadtree partitions, recent studies have shown that these are not efficient enough (in terms of rate-distortion performance) when images can be locally modeled as 2D piecewise-smooth signals. These observations motivate us to investigate the use of geometry based block partitioning for modeling intra data in video coding. In particular, in this paper, we study in detail the use of geometry-adaptive intra models, where wedgelet like discontinuities are used in order to define separate coding regions where different statistical/waveform modeling tools can be used. In order to implement this idea, we extend the existing H.264/AVC intra coding scheme by introducing two additional geometric modes: INTRA16X16GEO, and INTRA8X8GEO. Experimental results show that significantly improved R-D performance is achieved. |
