1 citations · 2 across the 13 of their papers we have counts for
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DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression
Junqi Shi, Ming Lu, Xingchen Li +3
Diffusion-based image compression has recently shown outstanding perceptual fidelity, yet its practicality is hindered by prohibitive sampling overhead and high memory usage. Most…
Reinforced Rate Control for Neural Video Compression via Inter-Frame Rate-Distortion Awareness
Wuyang Cong, Junqi Shi, Lizhong Wang +4
Neural video compression (NVC) has demonstrated superior compression efficiency, yet effective rate control remains a significant challenge due to complex temporal dependencies. Ex…
YODA: Yet Another One-step Diffusion-based Video Compressor
Xingchen Li, Junzhe Zhang, Junqi Shi +2
While one-step diffusion models have recently excelled in perceptual image compression, their application to video remains limited. Prior efforts typically rely on pretrained 2D au…
High-Efficiency Neural Video Compression via Hierarchical Predictive Learning
Ming Lu, Zhihao Duan, Wuyang Cong +3
The enhanced Deep Hierarchical Video Compression-DHVC 2.0-has been introduced. This single-model neural video codec operates across a broad range of bitrates, delivering not only s…
Accelerating block-level rate control for learned image compression
Muchen Dong, Ming Lu, Zhan Ma
Despite the unprecedented compression efficiency achieved by deep learned image compression (LIC), existing methods usually approximate the desired bitrate by adjusting a single qu…
Towards Backward-Compatible Continual Learning of Image Compression
Zhihao Duan, Ming Lu, Justin Yang +3
This paper explores the possibility of extending the capability of pre-trained neural image compressors (e.g., adapting to new data or target bitrates) without breaking backward co…