168 citations · 1.8k across the 114 of their papers we have counts for
116 papers
Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions
Qiang Qu, Xiaoming Chen, Vera Chung +1
In multimedia broadcasting, no-reference image quality assessment (NR-IQA) is used to indicate the user-perceived quality of experience (QoE) and to support intelligent data transm…
Light Field Compression Based on Implicit Neural Representation
Henan Wang, Hanxin Zhu, Zhibo Chen
Light field, as a new data representation format in multimedia, has the ability to capture both intensity and direction of light rays. However, the additional angular information a…
Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey
Xin Li, Yulin Ren, Xin Jin +5
Image restoration (IR) has been an indispensable and challenging task in the low-level vision field, which strives to improve the subjective quality of images distorted by various…
NVTC: Nonlinear Vector Transform Coding
Runsen Feng, Zongyu Guo, Weiping Li +1
In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance. Recent state-of-the-art methods for neural image c…
Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression
Yixin Gao, Runsen Feng, Zongyu Guo +1
Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significan…
Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts
Ruoyu Feng, Jinming Liu, Xin Jin +3
Image coding for machines (ICM) aims to compress images to support downstream AI analysis instead of human perception. For ICM, developing a unified codec to reduce information red…
Compression with Bayesian Implicit Neural Representations
Zongyu Guo, Gergely Flamich, Jiajun He +2
Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image. Based on this view,…
Semantically Structured Image Compression via Irregular Group-Based Decoupling
Ruoyu Feng, Yixin Gao, Xin Jin +2
Image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applicatio…
Inpaint Anything: Segment Anything Meets Image Inpainting
Tao Yu, Runseng Feng, Ruoyu Feng +4
Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first at…
Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective
Xin Li, Bingchen Li, Xin Jin +2
In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to…
Versatile Neural Processes for Learning Implicit Neural Representations
Zongyu Guo, Cuiling Lan, Zhizheng Zhang +2
Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Ne…
Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation
Xin Li, Cuiling Lan, Guoqiang Wei +1
Vision transformer has demonstrated great potential in abundant vision tasks. However, it also inevitably suffers from poor generalization capability when the distribution shift oc…
Task Residual for Tuning Vision-Language Models
Tao Yu, Zhihe Lu, Xin Jin +2
Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the well-learned kn…
MiNL: Micro-images based Neural Representation for Light Fields
Hanxin Zhu, Henan Wang, Zhibo Chen
Traditional representations for light fields can be separated into two types: explicit representation and implicit representation. Unlike explicit representation that represents li…
Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression
Xiaoshuai Fan, Xin Li, Zhibo Chen
As a commonly-used image compression format, JPEG has been broadly applied in the transmission and storage of images. To further reduce the compression cost while maintaining the q…
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation
Guangqi Xie, Xin Li, Shiqi Lin +4
The rapid development of intelligent tasks, e.g., segmentation, detection, classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compre…
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
Bingchen Li, Xin Li, Yiting Lu +3
Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts. Since the complex…
StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment
Yiting Lu, Xin Li, Jianzhao Liu +1
Deep neural networks (DNNs) have shown great potential in non-reference image quality assessment (NR-IQA). However, the annotation of NR-IQA is labor-intensive and time-consuming,…
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
Yiting Lu, Jun Fu, Xin Li +6
Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appr…
Image Coding for Machines with Omnipotent Feature Learning
Ruoyu Feng, Xin Jin, Zongyu Guo +6
Image Coding for Machines (ICM) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) a…
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment
Jianzhao Liu, Xin Li, Yanding Peng +2
Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment…
Deep Frequency Filtering for Domain Generalization
Shiqi Lin, Zhizheng Zhang, Zhipeng Huang +9
Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have unc…
Active Token Mixer
Guoqiang Wei, Zhizheng Zhang, Cuiling Lan +2
The three existing dominant network families, i.e., CNNs, Transformers, and MLPs, differ from each other mainly in the ways of fusing spatial contextual information, leaving design…
Mask-based Latent Reconstruction for Reinforcement Learning
Tao Yu, Zhizheng Zhang, Cuiling Lan +2
For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance. However, in practice, limited experience and h…