6 papers
Decoding Complexity-Rate-Quality Pareto-Front for Adaptive VVC Streaming
Angeliki Katsenou, Vignesh V Menon, Adam Wieckowski +2
Pareto-front optimization is crucial for addressing the multi-objective challenges in video streaming, enabling the identification of optimal trade-offs between conflicting goals s…
A CNN-based Post-Processor for Perceptually-Optimized Immersive Media Compression
Angeliki Katsenou, Fan Zhang, David Bull
In recent years, resolution adaptation based on deep neural networks has enabled significant performance gains for conventional (2D) video codecs. This paper investigates the effec…
VMAF-based Bitrate Ladder Estimation for Adaptive Streaming
Angeliki V. Katsenou, Fan Zhang, Kyle Swanson +3
In HTTP Adaptive Streaming, video content is conventionally encoded by adapting its spatial resolution and quantization level to best match the prevailing network state and display…
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…
Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming
Angeliki V. Katsenou, Joel Sole, David R. Bull
One of the challenges faced by many video providers is the heterogeneity of network specifications, user requirements, and content compression performance. The universal solution o…
Study of Compression Statistics and Prediction of Rate-Distortion Curves for Video Texture
Angeliki V. Katsenou, Mariana Afonso, David R. Bull
Encoding textural content remains a challenge for current standardised video codecs. It is therefore beneficial to understand video textures in terms of both their spatio-temporal…