1. Improved Techniques for Video Compression and Communication
- Author
-
Chen, Haoming
- Abstract
Video compression and communication has been an important field over the past decades and critical for many applications, e.g., video on demand, video-conferencing, and remote education. In many applications, providing low-delay and error-resilient video transmission and increasing the coding efficiency are two major challenges. Low-delay and error-resilient video coding is critical for real-time two-way video communication over wireless networks. Intra-refresh coding, which embeds intra coded regions into inter frames can achieve a relatively smooth bit-rate and terminate the error propagation caused by the transmission loss. In this dissertation, we propose a novel linear model for the intra-refresh cycle-size selection adapting to the network packet loss rates and the motions in the video content. We also propose a strategy that adapts the intra-refresh coding pattern to different cycle-size and obtains better R-D (Rate-Distortion) performance compared to traditional intra-refresh patterns. Experimental results show that the linear cycle-size selection model can achieve about 3dB improvement compared with a fixed cycle-size. Also, with the proposed intra-refresh order, a 2.0% bitrate reduction is obtained in average compared with the vertical-partition intra-refresh. The Intra Block Copy (IntraBC) is a newly adopted tool in the H.265/HEVC (High Efficiency Video Coding) extension for the screen content video coding. The IntraBC tool efficiently encodes repeating patterns in a picture. The current IntraBC scheme achieves about 1.0% bit-rate reduction on average and up to 4.3% bit-rate reduction on natural content video for a database consisting of 2K, 4K, and 8K sequences. In this dissertation, we propose to improve the IntraBC with a template matching block vector and a fractional search IntraBC. With these two tools, the gain on natural content video coding can be further improved by 0.5% on average and up to 2.0%. Intra prediction is an important tool in intra-frame video coding to reduce the spatial redundancy. In current coding standard H.265/HEVC, a copying-based method based on the boundary (or interpolated boundary) reference pixels is used to predict each pixel in the coding block to remove the spatial redundancy. We find that the conventional copying-based method can be further improved in two cases: 1) the boundary has an inhomogeneous region, and 2) the predicted pixel is far away from the boundary that the correlation between the predicted pixel and the reference pixels is relatively weak. This dissertation performs a theoretical analysis of the optimal weights based on a first-order Gaussian Markov model and the effects in these two cases. It also proposes a novel intra prediction scheme based on the analysis that smoothing the copying-based prediction can derive a better prediction block. Both the theoretical analysis of the coding gain and the experimental results show the effectiveness of the proposed intra prediction method. An average gain of 2.3% on all intra coding can be achieved with the H.265/HEVC reference software. Noise affects the efficiency of motion compensated prediction and degrades the coding performance. Some techniques, e.g., bi-directional prediction, have been utilized to decrease the effects of the noise. In this dissertation, we propose to further reduce noise and improve compression performance by applying a bilateral filter in the motion compensated prediction. The bilateral filter can remove the noise while preserving the edges. Our experimental results show that adding the bilateral filter can achieve a 4.9%, 1.6%, 1.6% average gain, in H.265/HEVC Low-Delay P (LDP), Low-Delay B (LDB), and Random Access (RA) coding, respectively, over a set of HD and UHD sequences up to 8K resolution. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]
- Published
- 2016