# Towards a Video Quality Assessment based Framework for Enhancement of Laparoscopic Videos

arXiv: https://arxiv.org/abs/2003.12679 · HTML: https://mlsanity.com/paper/2003.12679/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment](https://mlsanity.com/paper/2106.06784/) (2021)
- [A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment](https://mlsanity.com/paper/2202.04517/) (2022)
- [ExIQA: Explainable Image Quality Assessment Using Distortion Attributes](https://mlsanity.com/paper/2409.06853/) (2024)

## Abstract (arXiv)

Laparoscopic videos can be affected by different distortions which may impact the performance of surgery and introduce surgical errors. In this work, we propose a framework for automatically detecting and identifying such distortions and their severity using video quality assessment. There are three major contributions presented in this work (i) a proposal for a novel video enhancement framework for laparoscopic surgery; (ii) a publicly available database for quality assessment of laparoscopic videos evaluated by expert as well as non-expert observers and (iii) objective video quality assessment of laparoscopic videos including their correlations with expert and non-expert scores.