Video frame interpolation is a fundamental tool for temporal video enhancement, but existing quality metrics struggle to evaluate the perceptual impact of interpolation artefacts effectively. Metrics like PSNR, SSIM and LPIPS ignore temporal coherence. State-of-the-art quality metrics tailored towards video frame interpolation, like FloLPIPS, have been developed but suffer from computational inefficiency that limits their practical application. We present PSNRDIV, a novel full-reference quality metric that enhances PSNR through motion divergence weighting, a technique adapted from archival film restoration where it was developed to detect temporal inconsistencies. Our approach highlights singularities in motion fields which is then used to weight image errors. Evaluation on the BVI-VFI dataset (180 sequences across multiple frame rates, resolutions and interpolation methods) shows PSNRDIVachieves statistically significant improvements: +0.09 Pearson Linear Correlation Coefficient over FloLPIPS, while being 2.5× faster and using 4× less memory. Performance remains consistent across all content categories and are robust to the motion estimator used. The efficiency and accuracy of PSNRDIVenables fast quality evaluation and practical use as a loss function for training neural networks for video frame interpolation tasks. An implementation of our metric is available at www.github.com/conalld/psnr-div.

At practical streaming bitrates, traditional video compression pipelines frequently lead to visible artifacts that degrade perceptual quality. This submission couples the effectiveness of a neural post-processor with a different dynamic optimsation strategy for achieving an improved bitrate/quality compromise. The neural post-processor is refined via adversarial training and employs perceptual loss functions. By optimising the post-processor and encoder directly our method demonstrates significant improvement in video fidelity. The neural post-processor achieves substantial VMAF score increases of +6.72 and +1.81 at bitrates of 50 kb/s and 500 kb/s respectively.

The success of modern Deep Neural Network (DNN) approaches can be attributed to the use of complex optimization criteria beyond standard losses such as mean absolute error (MAE) or mean squared error (MSE). In this work, we propose a novel method of utilising a no-reference sharpness metric$Q$introduced by Zhu and Milanfar for removing out-of-focus blur from images. We also introduce a novel dataset of real-world out-of-focus images for assessing restoration models. Our fine-tuned method produces images with a 7.5% increase in perceptual quality (LPIPS) as compared to a standard model trained only on MAE. Furthermore, we observe a 6.7% increase in$Q$(reflecting sharper restorations) and 7.25% increase in PSNR over most state-of-the-art (SOTA) methods.

This work evaluates the effectiveness of modern video restoration methods, contrasting neural network-based techniques with traditional statistical algorithms to improve perceived video quality. Our analysis focused on three distinct methods: VBM4D, CVEGAN, and Ramsook, assessing their performance using pairwise subjective assessments with a compressed baseline. Results indicate a significant disparity between objective and subjective evaluations, with traditional methods like VBM4D showing limited improvements in perceptual quality, as demonstrated by a statistically non-significant increase in Mean-Opinion-Score (MOS). In contrast, the neural-based methods, CVEGAN and Ramsook, showed statistically significant improvements in subjective video quality. The findings highlight the superior capability of neural approaches to enhance perceptual quality, suggesting that current objective metrics may not fully capture quality as perceived by human observers. This study also contributes the results of the comparative analysis and the dataset to the research community.

Image based Deep Feature Quality Metrics (DFQMs) have been shown to better correlate with subjective perceptual scores over traditional metrics. The fundamental focus of these DFQMs is to exploit internal representations from a large scale classification network as the metric feature space. Previously, no attention has been given to the problem of identifying which layers are most perceptually relevant. In this paper we present a new method for selecting perceptually relevant layers from such a network, based on a neuroscience interpretation of layer behaviour. The selected layers are treated as a hyperparameter to the critic network in a W-GAN. The critic uses the output from these layers in the preliminary stages to extract perceptual information. A video enhancement network is trained adversarially with this critic. Our results show that the introduction of these selected features into the critic yields up to 10% (FID) and 15% (KID) performance increase against other critic networks that do not exploit the idea of optimised feature selection.

While video compression is necessary for large scale video streaming services, compression at low bitrate can degrade the original video and negatively affect the end user’s quality of experience. Deep Neural Networks (DNNs) are actively researched with respect to artifact removal, however the loss functions that are typically employed follows a derivation of a pixel-wise Lpnorm. In this paper we consider a DNN as a post-processor for video compression artifact removal. The DNN is trained using a composite perceptual loss that combines a traditional Lpnorm loss and a VMAF proxy network based on the Video Multimethod Assessment Function (VMAF). Results show an improvement in VMAF score over both the training and testing sets.

Many Telecommunications providers now support multiple generations of wireless standards in their network. For example, some providers simultaneously support 2G, 3G, 4G and even 5G standards. Upper management generally need a high level view of the performance being provided to consumers on a regular basis. In addition, engineers need to monitor the health of each technology in order to determine problems and be able to react quickly. We provide a framework for determination of a single Figure of Merit (FoM) that can be used for high level monitoring while at the same time providing sufficiently valuable low level indicators to assist with the isolation and detection of problems. We illustrate this framework using data from a real cellular network.

Modern Perceptual Visual Quality Metrics (PVQMs) for video are generally complex and non-differentiable. This makes them difficult to use as loss functions in restoration and compression tuning. Traditional metrics such as PSNR/MSE which are differentiable remain important but do not capture perceptual visual criteria. In this paper we present a DNN which models a popular perceptual video metric VMAF. In so doing, we introduce a differentiable loss function that closely matches the behaviour of a perceptual metric. Employing degradation generated with H.265 compression, our model achieves a 4.41% RMSE in predicting VMAF. This can now be deployed as a video based loss function in video enhancement and compression tasks.