Published in IEEE ICIP 2025, 2025
Introduces ARaBIQA, a blind image quality assessment method tailored to augmented reality content, with emphasis on distortions specific to immersive visual pipelines.
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Published in IEEE ICASSP 2025, 2025
A ranking-guided training strategy for lightweight image quality prediction that integrates perceptual ordering feedback to improve model calibration and pairwise consistency.
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Published in arXiv preprint / later journal extension, 2024
This work studies knowledge distillation for AR image quality assessment, improving compact model representations and preserving perceptual sensitivity in no-reference quality prediction.
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Published in IEEE International Workshop on Multimedia Signal Processing (MMSP 2024), 2024
A lightweight transformer-based approach for perceptual quality assessment in augmented reality, designed to reduce computational cost while preserving prediction performance on subjective AR quality datasets.
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Published in 32nd European Signal Processing Conference (EUSIPCO 2024), 2024
Conventional imaging diagnostics frequently encounter bottlenecks due to manual inspection, which can lead to delays and inconsistencies. Although deep learning offers a pathway to automation and enhanced accuracy, foundational models in computer vision often emphasise global context at the expense of the local structures that clinicians rely on.
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Published in Digital Image Security, 2024
Since the advent of digital cameras, image editing tools have made it straightforward to manipulate content. Copy-move forgeries—where a region is duplicated and pasted within the same frame—are particularly challenging for human observers to detect, yet they threaten the credibility of visual evidence.
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Published in 20th International Conference on Content-Based Multimedia Indexing (CBMI 2023), 2023
Knee osteoarthritis is a widespread condition that can cause chronic pain and loss of mobility. Early detection and grading are critical for effective intervention, yet manual assessment remains labour intensive.
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Published in IEEE International Conference on Image Processing (ICIP 2022), 2022
Predicting the perceptual quality of medical imagery is crucial for diagnostic workflows, yet annotated data are scarce and quality metrics must remain reliable across acquisition domains. We introduce a lightweight convolutional self-attention architecture that interpolates global quality scores from local perceptual cues while remaining data efficient.
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