Semantically-Aware Game Image Quality Assessment
Kai Zhu, Vignesh Edithal, Le Zhang, Ilia Blank, Imran Junejo

TL;DR
This paper introduces a semantically-aware no-reference image quality assessment model specifically designed for video game graphics, addressing unique distortions and outperforming existing methods in gaming contexts.
Contribution
The study presents a novel NR-IQA model with a game-specific distortion feature extractor and semantic gating, tailored for gaming distortions and trained on gameplay data.
Findings
Generalizes well to unseen distortion levels
Outperforms out-of-domain quality assessment methods
Provides robust, monotonic quality trends across games
Abstract
Assessing the visual quality of video game graphics presents unique challenges due to the absence of reference images and the distinct types of distortions, such as aliasing, texture blur, and geometry level of detail (LOD) issues, which differ from those in natural images or user-generated content. Existing no-reference image and video quality assessment (NR-IQA/VQA) methods fail to generalize to gaming environments as they are primarily designed for distortions like compression artifacts. This study introduces a semantically-aware NR-IQA model tailored to gaming. The model employs a knowledge-distilled Game distortion feature extractor (GDFE) to detect and quantify game-specific distortions, while integrating semantic gating via CLIP embeddings to dynamically weight feature importance based on scene content. Training on gameplay data recorded across graphical quality presets enables…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Video Quality Assessment · Video Analysis and Summarization · Visual Attention and Saliency Detection
MethodsContrastive Language-Image Pre-training · ALIGN · Knowledge Distillation
