# Image quality prediction using synthetic and natural codebooks: comparative results

arXiv: https://arxiv.org/abs/2212.10319 · HTML: https://mlsanity.com/paper/2212.10319/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [On scale-invariant properties in natural images and their simulations](https://mlsanity.com/paper/2201.13312/) (2022)
- [Curiously Effective Features for Image Quality Prediction](https://mlsanity.com/paper/2106.05946/) (2021)

## Abstract (arXiv)

We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORNIA model. We analyze the codebook building method and propose some modifications for it. Also the algorithm is investigated from the point of inference time reduction. Both natural and synthetic images are used for building codebooks and some analysis of synthetic images used for codebooks is provided. It is demonstrated the results on quality assessment may be improves with the use if synthetic images for codebook construction. We also demonstrate regimes of the algorithm in which real time execution on CPU is possible for sufficiently high correlations with mean opinion score (MOS). Various pooling strategies are considered as well as the problem of metric sensitivity to bitrate.