64 citations · 288 across the 18 of their papers we have counts for
19 papers
Evaluating quality metrics through the lenses of psychophysical measurements of low-level vision
Dounia Hammou, Yancheng Cai, Pavan Madhusudanarao +2
Image and video quality metrics, such as SSIM, LPIPS, and VMAF, aim to predict perceived visual quality and are often assumed to reflect principles of human vision. However, relati…
AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content
Marcos V Conde, Zhijun Lei, Wen Li +3
Video super-resolution (VSR) is a critical task for enhancing low-bitrate and low-resolution videos, particularly in streaming applications. While numerous solutions have been deve…
Estimating the Resize Parameter in End-to-end Learned Image Compression
Li-Heng Chen, Christos G. Bampis, Zhi Li +2
We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our approach is simple: compose a pai…
Banding vs. Quality: Perceptual Impact and Objective Assessment
Lukáš Krasula, Zhi Li, Christos G. Bampis +3
Staircase-like contours introduced to a video by quantization in flat areas, commonly known as banding, have been a long-standing problem in both video processing and quality asses…
Convolutional Block Design for Learned Fractional Downsampling
Li-Heng Chen, Christos G. Bampis, Zhi Li +2
The layers of convolutional neural networks (CNNs) can be used to alter the resolution of their inputs, but the scaling factors are limited to integer values. However, in many imag…
Enhancing VMAF through New Feature Integration and Model Combination
Fan Zhang, Angeliki Katsenou, Christos Bampis +3
VMAF is a machine learning based video quality assessment method, originally designed for streaming applications, which combines multiple quality metrics and video features through…