Encoding in the Dark

Submission Dates:
11/06/2019 to 04/03/2020
Citation Author(s):
Nantheera
Anantrasirichai
University of Bristol
Paul
Hill
University of Bristol
Angeliki
Katsenou
University of Bristol
Alexandra
Malyugina
University of Bristol
Fan
Zhang
University of Bristol
Submitted by:
Angeliki Katsenou
Last updated:
Fri, 05/01/2020 - 09:40
DOI:
10.21227/gffb-sq72
Data Format:
Links:
License:
Creative Commons Attribution

Abstract 

Low light scenes often come with acquisition noise, which not only disturbs the viewers, but it also makes video compression harder. These type of videos are often encountered in cinema as a result of artistic perspective or the nature of a scene. Other examples include shots of wildlife (e.g. mobula rays at night in Blue Planet II), concerts and shows, surveillance camera footage and more. Inspired by all above, we are proposing a challenge on encoding low-light captured videos. This challenge intends to identify technology that improves the perceptual quality of compressed low-light videos beyond the current state of the art performance of the most recent coding standards, such as HEVC, AV1, VVC etc. Moreover, this will offer a good opportunity for both experts in the fields of video coding and image enhancement to address this problem. A series of subjective tests will be part of the evaluation, the results of which can be used in a study of the tradeoff between artistic direction and the viewers' preferences, such as mystery movies and some investigation scenes in the film.

Instructions: 

Participants will be requested to deliver bitstreams with pre-defined maximum target rates for a given set of sequences, a short report (if they do not want to submit a conference paper or have already submitted one in ICME2020 as a regular submission) describing their contribution and a software executable for running the proposed methodology and then can reconstruct the decoded videos by the given timeline. 

Comments

Request for access

How to submit our competition data?

Dataset Files

You must be an approved participant in this data competition to access dataset files. To request access you must first login.

Login