# Multi-task deep CNN model for no-reference image quality assessment on smartphone camera photos

arXiv: https://arxiv.org/abs/2008.11961 · HTML: https://mlsanity.com/paper/2008.11961/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [An Image Quality Assessment Dataset for Portraits](https://mlsanity.com/paper/2304.05772/) (2023)

## Abstract (arXiv)

Smartphone is the most successful consumer electronic product in today's mobile social network era. The smartphone camera quality and its image post-processing capability is the dominant factor that impacts consumer's buying decision. However, the quality evaluation of photos taken from smartphones remains a labor-intensive work and relies on professional photographers and experts. As an extension of the prior CNN-based NR-IQA approach, we propose a multi-task deep CNN model with scene type detection as an auxiliary task. With the shared model parameters in the convolution layer, the learned feature maps could become more scene-relevant and enhance the performance. The evaluation result shows improved SROCC performance compared to traditional NR-IQA methods and single task CNN-based models.