most citedCausal Context Adjustment Loss for Learned Image Compression

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks

Xiaoye Liang, Lai Jiang, Minglang Qiao +6

In recent years, the development of burst imaging technology has improved the capture and processing capabilities of visual data, enabling a wide range of applications. However, th…

cs.CV2025

NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results

Nikolay Safonov, Alexey Bryncev, Andrey Moskalenko +28

This paper presents an overview of the NTIRE 2025 Challenge on UGC Video Enhancement. The challenge constructed a set of 150 user-generated content videos without reference ground…

cs.CV2025

SMILENet: Unleashing Extra-Large Capacity Image Steganography via a Synergistic Mosaic InvertibLE Hiding Network

Jun-Jie Huang, Zihan Chen, Tianrui Liu +5

Existing image steganography methods face fundamental limitations in hiding capacity (typically images) due to severe information interference and uncoordinated capacity-d…

cs.CV2025

CODA: Repurposing Continuous VAEs for Discrete Tokenization

Zeyu Liu, Zanlin Ni, Yeguo Hua +4

Discrete visual tokenizers transform images into a sequence of tokens, enabling token-based visual generation akin to language models. However, this process is inherently challengi…

eess.IV20241 cited

Causal Context Adjustment Loss for Learned Image Compression

Minghao Han, Shiyin Jiang, Shengxi Li +4

In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniq…