Recent advances in deep learning have greatly improved No-Reference Video Quality Assessment (NR-VQA), with fragment-based approaches significantly reducing computational complexity through random patch sampling. However, challenges remain due to the reliance on random sampling and the uniform weighting of patches, which limit global observation effectiveness. To address these issues, we propose Saliency Fragments No-Reference Video Quality Assessment (SF-VQA), a novel method prioritizing salient regions. SF-VQA employs Saliency Grid Mini Sampling (SGMS) to select patches near areas of interest and utilizes the Saliency Grid Score (SGS) to compute a weighted quality score based on grid relevance. Experimental results demonstrate that SF-VQA achieves superior performance in Spearman’s Rank Correlation Coefficient (SRCC) and Pearson’s Linear Correlation Coefficient (PLCC) with minimal parameter increase, outperforming existing NR-VQA methods. This demonstrates SF-VQA’s efficiency and effectiveness in advancing NR-VQA.

Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN). However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator is ineffective as an anomaly detector. In this paper, we propose modifications to the GAN loss such that the generated samples lie at the boundaries of the real data distribution. With our modified GAN loss, our anomaly detection method, called Fence GAN (FGAN), directly uses the discriminator score as an anomaly threshold. Our experimental results on the MNIST, CIFAR10 and KDD99 datasets show that FGAN yields the best anomaly classification accuracy compared to state-of-the-art methods.