3 research outputs found
A Neural-network Enhanced Video Coding Framework beyond ECM
In this paper, a hybrid video compression framework is proposed that serves
as a demonstrative showcase of deep learning-based approaches extending beyond
the confines of traditional coding methodologies. The proposed hybrid framework
is founded upon the Enhanced Compression Model (ECM), which is a further
enhancement of the Versatile Video Coding (VVC) standard. We have augmented the
latest ECM reference software with well-designed coding techniques, including
block partitioning, deep learning-based loop filter, and the activation of
block importance mapping (BIM) which was integrated but previously inactive
within ECM, further enhancing coding performance. Compared with ECM-10.0, our
method achieves 6.26, 13.33, and 12.33 BD-rate savings for the Y, U, and V
components under random access (RA) configuration, respectively
Fast Algorithm Designs of Multiple-Mode Discrete Integer Transforms with Cost-Effective and Hardware-Sharing Architectures for Multistandard Video Coding Applications
In this chapter, first we give a brief view of transform-based video coding. Second, the basic matrix decomposition scheme for fast algorithm and hardware-sharing-based integer transform design are described. Finally, two case studies for fast algorithm and hardware-sharing-based architecture designs of discrete integer transforms are presented, where one is for the single-standard multiple-mode video transform-coding application, and the other is for the multiple-standard multiple-mode video transform-coding application
Architectures for Adaptive Low-Power Embedded Multimedia Systems
This Ph.D. thesis describes novel hardware/software architectures for adaptive low-power embedded multimedia systems. Novel techniques for run-time adaptive energy management are proposed, such that both HW & SW adapt together to react to the unpredictable scenarios. A complete power-aware H.264 video encoder was developed. Comparison with state-of-the-art demonstrates significant energy savings while meeting the performance constraint and keeping the video quality degradation unnoticeable