Towards a Video Quality Assessment based Framework for Enhancement of Laparoscopic Videos
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Mar 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 2021Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessmentcited by 3
- 2022A Neural Network based Framework for Effective Laparoscopic Video Quality Assessmentcited by 2
- 2024ExIQA: Explainable Image Quality Assessment Using Distortion Attributescited by 0
- 2024ExIQA: Explainable Image Quality Assessment Using Distortion Attributes10 Sep 2024
- 2022A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment9 Feb 2022
- 2021Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment12 Jun 2021