.png){kind=icon}
.png){kind=icon}
Return
Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2016.2621666.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Optimal rate allocation is among the most challenging tasks to perform in the context of predictive video coding, because of the dependencies between frames induced by motion compensation. In this paper, using a recursive rate-distortion model that explicitly takes into account these dependencies, we approach the frame-level rate allocation as a convex optimization problem. This technique is integrated into the recent HEVC encoder, and tested on several standard sequences. Experiments indicate that the proposed rate allocation ensures a better performance (in the rate-distortion sense) than the standard HEVC rate control, and with a little loss with respect to an optimal exhaustive research, which is largely compensated by a much shorter execution time.
Keywords:
Video coding
rate distortion
convex optimization
bit allocation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W