Latent Image and Video Resolution Prediction using Convolutional Neural Networks
This addresses a previously overlooked Video Quality Assessment problem for detecting upscaled media, though it appears incremental as it applies existing CNNs to a new dataset.
The paper tackles the latent resolution prediction problem, where images or videos are upscaled and misreported as higher resolution, by formulating it, constructing a dataset, and introducing machine learning algorithms including CNNs, achieving about 95% accuracy in predicting latent video resolution.
This paper introduces a Video Quality Assessment (VQA) problem that has received little attention in the literature, called the latent resolution prediction problem. The problem arises when images or videos are upscaled from their native resolution and are reported as having a higher resolution than their native resolution. This paper formulates the problem, constructs a dataset for training and evaluation, and introduces several machine learning algorithms, including two Convolutional Neural Networks (CNNs), to address this problem. Experiments indicate that some proposed methods can predict the latent video resolution with about 95% accuracy.