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

TL;DR

This paper introduces a multi-task deep CNN model for no-reference image quality assessment of smartphone photos, leveraging scene type detection to improve quality prediction accuracy.

Contribution

It proposes a novel multi-task CNN approach that jointly learns image quality and scene type, enhancing feature relevance and assessment performance.

Findings

Improved SROCC performance over traditional methods

Multi-task learning enhances feature relevance for quality assessment

Scene type detection benefits image quality prediction

Abstract

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.