PV '19- Proceedings of the 24th ACM Workshop on Packet Video

PV '19- Proceedings of the 24th ACM Workshop on Packet Video

Full Citation in the ACM Digital Library

Context-adaptive recursive-filtering-based intra prediction in video coding

  •      Hui Su
  • Alexander Bokov
  • Urvang Joshi
  • Debargha Mukherjee
  • Jingning Han
  • Yue Chen

Conventional intra prediction modes in image and video coding generate an estimation of a target block by copying or projecting its causal neighboring pixels along certain angles. Such simple directional model does not work well for complex image structures. A set of context-adaptive intra prediction modes based on recursive filtering is proposed in this paper. The prediction of a block is generated by applying linear filtering over certain previously reconstructed or predicted pixels in the causal neighborhood of each pixel recursively. The filter coefficients are estimated with least squares optimization using previously reconstructed pixels in the above and/or left regions of the current block. The configurations for the filters such as filter taps, position of reference pixels, as well as the location and shape of the training regions are all flexible, making the proposed prediction modes highly adaptive to local image texture contexts. A data-driven approach is used to select the optimal subset of all the possible filter configurations while retaining as much coding gains as possible. The proposed approach is tested on the state-of-the-art AV1 video coding standard. AV1 supports sophisticated intra prediction tools such as recursive filtering, quadratic interpolation filtering, intra block-copy, and the palette mode. Experimental results show that the context-adaptive recursive-filtering-based intra prediction modes can achieve significant improvement in compression efficiency.

Streaming 360° videos to head-mounted virtual reality using DASH over QUIC transport protocol

  •      Shou-Cheng Yen
  • Ching-Ling Fan
  • Cheng-Hsin Hsu

We design, implement, and evaluate a tiled DASH streaming system for 360° videos using QUIC/UDP protocol, in which multiplexed and prioritized streams are leveraged for sending urgent tiles that are about to miss their playout time. In particular, we develop a new architecture to concurrently request for regular tiled segments at a lower priority and urgent tiled segments at a higher priority as multiple streams over a single QUIC connection. Several core components, including the fixation prediction algorithm, fast tile selector, and Adaptive Bit Rate (ABR) controller are designed for this new architecture. Our trace-driven experiments reveal that: (i) DASH streaming over the QUIC protocol outperforms doing that over the HTTP/1.1 and HTTP/2 stacks and (ii) our urgent tiled segments reduce the missing ratio and increase the video quality without incurring excessive bandwidth utilization under diverse network bandwidth, user behavior, and video characteristics.