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- Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images37
An effective deep learning–based no-reference no-reference IQA model is proposed by utilizing both distortion and aesthetic quality representation and outperforms all other state-of-the-art NR IQA methods on the constructed CGIQA-6k database and other CGIQA-related databases.
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- StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability31
A new database named StableDB is built that contains 1,952 diversely-shaky UGC videos, where each video has a Mean Opinion Score on the degree of video stability rated by 34 subjects and a novel VQA-S model named St stableVQA, which consists of three feature extractors to acquire the optical flow, semantic, and blur features respectively, and a regression layer to predict the final stability score.
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- Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model27
The Unify Generated Video Quality Assessment (UGVQ) model is proposed, designed to accurately evaluate the multi-dimensional quality of AIGC videos, and established a benchmark for evaluating existing quality assessment metrics on the LGVQ dataset.
- Enhancing blind video quality assessment with rich quality-aware features27
This paper proposes Rich Quality-aware features enabled Video Quality Assessment (RQ-VQA), a simple yet effective method to enhance BVQA by leveraging rich quality-aware features extracted from off-the-shelf BIQA and BVQA models.
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- Fine localization and distortion resistant detection of multi-class barcode in complex environments22
A region-based end-to-end network to finely localize and classify 1D barcode and Quick Response (QR) code in complex environments is proposed and it is demonstrated that the method can resist some distortions by simulating barcode images of different image qualities.
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- Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric20
An objective evaluation metric, named AI-Generated Human activity Video Quality metric (GHVQ), is developed, to automatically analyze the quality of human activity AGVs, and outperforms existing quality metrics on the Human-AGVQA dataset by a large margin.
- Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey20
The NTIRE 2024 Portrait Quality Assessment Challenge is reviewed, highlighting the proposed solutions and results, and providing a gauge for the current state-of-the-art in Portrait Quality Assessment.
- 3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents19
This paper introduces a novel 3DGC quality assessment dataset, 3DGCQA, built using 7 representative Text-to-3D generation methods, and reveals the presence of 6 common distortion categories in the generated 3DGCs.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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