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most citedCausal Context Adjustment Loss for Learned Image Compression

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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

Rethinking Diffusion Model-Based Video Super-Resolution: Leveraging Dense Guidance from Aligned Features

Jingyi Xu, Meisong Zheng, Ying Chen +3

Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. However, they suffer from error accumulation, spatial artifacts, and a tra…

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…