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arXiv:2609.27560v1 Announce Type: cross Abstract: Avatar-streaming systems are commonly evaluated with image and video quality assessment (IQA/VQA) metrics, implicitly treating visual fidelity as a p...
arXiv:2610.00677v1 Announce Type: new Abstract: Advancements in augmented reality (AR) continue to foster innovative solutions, facilitating novel methodologies within educational systems, healthcare...
LLaVA‑Assessor is a unified large multi‑modal model (LMM) designed for visual quality assessment, combining image and video inputs. It introduces a two‑task framework—quality interpretation and quality scoring—supported by an adaptive architecture, a rigorous human‑annotated dataset, and a machine‑synthesized data expansion pipeline. The model employs a prompt‑disentanglement strategy to stabilize multi‑task training and achieves strong performance across 11 quality scoring test sets and 4 interpretation benchmarks.
arXiv:2607. 01086v1 Announce Type: cross Abstract: The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs).
PreResQ‑R1 introduces a Preference‑Response Disentangled Reinforcement Learning framework for Visual Quality Assessment that jointly optimizes absolute score regression and relative ranking consistency. It employs a dual‑branch reward system—modeling intra‑sample response coherence and inter‑sample preference alignment—trained with Group Relative Policy Optimization. The method extends to video quality assessment via a global‑temporal and local‑spatial data flow strategy, achieving state‑of‑the‑art results on 10 IQA and 5 VQA benchmarks with only 6K images and 28K videos, and provides human‑aligned reasoning traces.
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shift, structural damage, and over-exposure, which degrade the perceptual quality of the enhanced images.