Encoding in the Dark Grand Challenge:An Overview

A big part of the video content we consume from video providers consists of genres featuring low-light aesthetics. Low light sequences have special characteristics, such as spatio-temporal varying acquisition noise and light flickering, that make the encoding process challenging. To deal with the spatio-temporal incoherent noise, higher bitrates are used to achieve high objective quality. Additionally, the quality assessment metrics and methods have not been designed, trained or tested for this type of content. This has inspired us to trigger research in that area and propose a Grand Challenge on encoding low-light video sequences. In this paper, we present an overview of the proposed challenge, and test state-of-the-art methods that will be part of the benchmark methods at the stage of the participants' deliverable assessment. From this exploration, our results show that VVC already achieves a high performance compared to simply denoising the video source prior to encoding. Moreover, the quality of the video streams can be further improved by employing a post-processing image enhancement method

BVI-LOWLIGHT

One of the weak points of most of denoising algoritms (deep learning based ones) is the training data. Due to no or very limited amount of groundtruth data available, these algorithms are often evaluated using synthetic noise models such as Additive Zero-Mean Gaussian noise. The downside of this approach is that these simple model do not represent noise present in natural imagery. For evaluation of denoising algorithms’ performance in poor light conditions, we need either representative models or real noisy images paired with those we can consider as groundtruth

BVI-Lowlight: Fully registered datasets for low-light image and video enhancement

Low-light images and video footage often exhibit issues due to the interplay of various parameters such as aperture, shutter speed, and ISO settings. These interactions can lead to distortions, especially in extreme lighting conditions. This distortion is primarily caused by the inverse relationship between decreasing light intensity and increasing photon noise, which gets amplified with higher sensor gain. Additionally, secondary characteristics like white balance and color effects can also be adversely affected and may require post-processing correction. These distortions not only impact the perceived quality of the images but also pose significant challenges for machine learning tasks, including classification and object detection. This is particularly evident when considering the susceptibility of deep learning networks to adversarial examples.The BVI-Lowlight datasets offer fully registered low-light content alongside their corresponding clean and normal light condition ones. This dataset includes both images and videos, enabling the use of supervised learning approaches and performance evaluation through objective metrics such as PSNR and SSIM