11 papers
Decoder-Guided Lossy Contour Coding Via Anchor Refinement
Ruoyu Yang, Yichi Zhang, Haohong Wang +1
Object contours serve as compact structural priors for many receiver-side vision tasks such as image super-resolution, edge-conditioned generation, and machine vision. When such ta…
Uni-LVC: A Unified Method for Intra- and Inter-Mode Learned Video Compression
Yichi Zhang, Ruoyu Yang, Fengqing Zhu
Recent advances in learned video compression (LVC) have led to significant performance gains, with codecs such as DCVC-RT surpassing the H.266/VVC low-delay mode in compression eff…
Adaptive Greedy Frame Selection for Long Video Understanding
Yuning Huang, Xiaoyu Ji, Joseph Huang +2
Large vision--language models (VLMs) are increasingly applied to long-video question answering, yet inference is often bottlenecked by the number of input frames and resulting visu…
Leveraging Second-Order Curvature for Efficient Learned Image Compression: Theory and Empirical Evidence
Yichi Zhang, Fengqing Zhu
Training learned image compression (LIC) models entails navigating a challenging optimization landscape defined by the fundamental trade-off between rate and distortion. Standard f…
OpenDCVCs: A PyTorch Open Source Implementation and Performance Evaluation of the DCVC series Video Codecs
Yichi Zhang, Fengqing Zhu
We present OpenDCVCs, an open-source PyTorch implementation designed to advance reproducible research in learned video compression. OpenDCVCs provides unified and training-ready im…
Low-Rank Adaptation of Pre-trained Vision Backbones for Energy-Efficient Image Coding for Machine
Yichi Zhang, Zhihao Duan, Yuning Huang +1
Image Coding for Machines (ICM) focuses on optimizing image compression for AI-driven analysis rather than human perception. Existing ICM frameworks often rely on separate codecs f…
Accelerating Learned Image Compression Through Modeling Neural Training Dynamics
Yichi Zhang, Zhihao Duan, Yuning Huang +1
As learned image compression (LIC) methods become increasingly computationally demanding, enhancing their training efficiency is crucial. This paper takes a step forward in acceler…
Balanced Rate-Distortion Optimization in Learned Image Compression
Yichi Zhang, Zhihao Duan, Yuning Huang +1
Learned image compression (LIC) using deep learning architectures has seen significant advancements, yet standard rate-distortion (R-D) optimization often encounters imbalanced upd…
On Efficient Neural Network Architectures for Image Compression
Yichi Zhang, Zhihao Duan, Fengqing Zhu
Recent advances in learning-based image compression typically come at the cost of high complexity. Designing computationally efficient architectures remains an open challenge. In t…
Theoretical Bound-Guided Hierarchical VAE for Neural Image Codecs
Yichi Zhang, Zhihao Duan, Yuning Huang +1
Recent studies reveal a significant theoretical link between variational autoencoders (VAEs) and rate-distortion theory, notably in utilizing VAEs to estimate the theoretical upper…
Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding
Yichi Zhang, Zhihao Duan, Ming Lu +3
While convolution and self-attention are extensively used in learned image compression (LIC) for transform coding, this paper proposes an alternative called Contextual Clustering b…