eprintid: 2590
rev_number: 10
eprint_status: archive
userid: 311
dir: disk0/00/00/25/90
datestamp: 2017-10-29 03:19:37
lastmod: 2017-10-29 03:19:37
status_changed: 2017-10-29 03:19:37
type: conference_item
metadata_visibility: show
creators_name: Hoang, Van Xiem
creators_id: xiemhoang@vnu.edu.vn
title: Improving SHVC Performance with a Block based Joint Layer Prediction Solution
ispublished: inpress
subjects: ECE
divisions: fac_fet
abstract: Considering for the need of a more powerful scalable video coding solution beyond the recent Scalable High Efficiency Video Coding (SHVC) standard, this paper proposes a novel joint layer prediction creation solution. In the proposed improvement solution, the temporal correlation between frames is exploited through a motion compensated temporal interpolation (MCTI) mechanism. The MCTI frame is then adaptively combined with the base layer reconstruction using a linear combination algorithm. In this combination, a weighting factor is defined and computed for each predicted block using the estimated errors associated to each input. Finally, to achieve the highest compression efficiency, the fused frame is treated as an additional reference and adaptively selected using a rate distortion optimization (RDO) mechanism. Experiments conducted for a rich set of test conditions have shown that significant compression efficiency gains can be achieved with the proposed improvement solution, notably by up to 4.5 % in enhancement layer BD-Rate savings regarding the standard SHVC quality scalable codec.
date: 2018-01
date_type: published
full_text_status: public
pres_type: lecture
pagerange: 1-4
event_title: International Workshop on Advanced Image Technology 2018
event_location: ChiangMai, Thailand
event_dates: Jan, 2018
event_type: workshop
refereed: TRUE
funders: Nafosted
projects: 102.01- 2016.15
citation:   Hoang, Van Xiem  (2018) Improving SHVC Performance with a Block based Joint Layer Prediction Solution.  In: International Workshop on Advanced Image Technology 2018, Jan, 2018, ChiangMai, Thailand.    (In Press)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2590/1/IWAIT%202018_Joint%20Layer%20SHVC.pdf