Video Quality Assessment

In the context of image enhancement, maximizing NIMA score as a prior can increase the likelihood of enhancing perceptual quality of an image.

ZFNet is a classic convolutional neural network. The design was motivated by visualizing intermediate feature layers and the operation of the classifier. Compared to AlexNet, the filter sizes are reduced and the stride of the convolutions are reduced.

MUSIQ, or Multi-scale Image Quality Transformer, is a Transformer-based model for multi-scale image quality assessment. It processes native resolution images with varying sizes and aspect ratios. In MUSIQ, we construct a multi-scale image representation as input, including the native resolution image and its ARP resized variants. Each image is split into fixed-size patches which are embedded by a patch encoding module (blue boxes). To capture 2D structure of the image and handle images of varying aspect ratios, the spatial embedding is encoded by hashing the patch position $(i,j)$ to $(t_{i},t_{j})$ within a grid of learnable embeddings (red boxes). Scale Embedding (green boxes) is introduced to capture scale information. The Transformer encoder takes the input tokens and performs multi-head self-attention. To predict the image quality, MUSIQ follows a common strategy in Transformers to add an [CLS] token to the sequence to represent the whole multi-scale input and the corresponding Transformer output is used as the final representation.