73 citations · 289 across the 50 of their papers we have counts for
12 papers · 1 filter
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution
Yuxuan Jiang, Ho Man Kwan, Tianhao Peng +5
Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (I…
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content
Yuxuan Jiang, Jakub Nawała, Chen Feng +4
Super-resolution (SR) is a key technique for improving the visual quality of video content by increasing its spatial resolution while reconstructing fine details. SR has been emplo…
PNVC: Towards Practical INR-based Video Compression
Ge Gao, Ho Man Kwan, Fan Zhang +1
Neural video compression has recently demonstrated significant potential to compete with conventional video codecs in terms of rate-quality performance. These learned video codecs…
NVRC: Neural Video Representation Compression
Ho Man Kwan, Ge Gao, Fan Zhang +2
Recent advances in implicit neural representation (INR)-based video coding have demonstrated its potential to compete with both conventional and other learning-based approaches. Wi…
BVI-UGC: A Video Quality Database for User-Generated Content Transcoding
Zihao Qi, Chen Feng, Fan Zhang +3
In recent years, user-generated content (UGC) has become one of the major video types consumed via streaming networks. Numerous research contributions have focused on assessing its…
Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration
Siyue Teng, Yuxuan Jiang, Ge Gao +4
Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of t…
BVI-AOM: A New Training Dataset for Deep Video Compression Optimization
Jakub Nawała, Yuxuan Jiang, Fan Zhang +3
Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs. These learning-based methods typically require diverse and represen…
MVAD: A Multiple Visual Artifact Detector for Video Streaming
Chen Feng, Duolikun Danier, Fan Zhang +3
Visual artifacts are often introduced into streamed video content, due to prevailing conditions during content production and delivery. Since these can degrade the quality of the u…
RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content
Tianhao Peng, Chen Feng, Duolikun Danier +4
With recent advances in deep learning, numerous algorithms have been developed to enhance video quality, reduce visual artifacts, and improve perceptual quality. However, little re…
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
Yuxuan Jiang, Chen Feng, Fan Zhang +1
Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performa…
Immersive Video Compression using Implicit Neural Representations
Ho Man Kwan, Fan Zhang, Andrew Gower +1
Recent work on implicit neural representations (INRs) has evidenced their potential for efficiently representing and encoding conventional video content. In this paper we, for the…
Compressing Deep Image Super-resolution Models
Yuxuan Jiang, Jakub Nawala, Fan Zhang +1
Deep learning techniques have been applied in the context of image super-resolution (SR), achieving remarkable advances in terms of reconstruction performance. Existing techniques…