| Autor: |
Fotio Tiotsop, Lohic, Servetti, Antonio, Pocta, Peter, Van Wallendael, Glenn, Barkowsky, Marcus, Masala, Enrico |
| Předmět: |
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| Zdroj: |
ACM Transactions on Multimedia Computing, Communications & Applications; Aug2024, Vol. 20 Issue 8, p1-27, 27p |
| Abstrakt: |
A recent research direction is focused on training Deep Neural Networks (DNNs) to replicate individual subject assessments of media quality. These DNNs are referred to as Artificial Intelligence-based Observers (AIOs). An AIO is designed to simulate, in real-time, the quality ratings of a specific individual, enabling an automatic quality assessment that accounts for subjects characteristics and preferences. Training AIOs is a promising but challenging research area due to the greater noise in individual raw opinion scores compared to the Mean Opinion Score. Effective learning from noisy labels necessitates the training of complex models on large-scale datasets. Unfortunately, this is challenging for AIOs as the media quality assessment community lacks extensive datasets that include individual opinion scores. To address the complexity of the task, we first created a dataset comprising two million samples, with synthetic labels derived from human annotation. We then trained a customized network for image quality assessment, named Multi-Distortion ResNet50 (MDResNet50), on this dataset. The weights of the MDResNet50 were subsequently utilized to initialize the learning process of each AIO, thereby avoiding the need to train a complex model from scratch on a small-scale dataset with raw individual opinion scores. Computational experiments show that our approach significantly advances the state-of-the-art in the AIO research. In particular: (i) we demonstrate through a simulation the ability of AIOs to mimic two well-known behavioral characteristics of a subject, i.e., bias and inconsistency, when scoring the media quality; (ii) we train and release DNN-based AIOs that, compared to the state-of-the-art, exhibit a higher performance with a statistical significance in assessing multiple image distortions; (iii) we train AIOs that more accurately mimic the sensitivity of real subjects to noise and color saturation and also better predict the opinion score distribution compared to the state-of-the-art AIOs. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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