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Latent Image and Video Resolution Prediction using Convolutional Neural Networks

2024-10-17 · Rittwika Kansabanik, Adrian Barbu

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.

📄 PDF Abstract BibTeX arXiv:2410.13227

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Video Quality AssessmentVisual Question Answering (VQA)

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Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
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