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

• URL: http://arxiv.org/abs/2008.11961v1
• Date: Thu, 27 Aug 2020 07:33:05 GMT
• Title: Multi-task deep CNN model for no-reference image quality assessment on smartphone camera photos
• Authors: Chen-Hsiu Huang, Ja-Ling Wu
• Abstract summary: 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. The evaluation result shows improved SROCC performance compared to traditional NR-IQA methods and single task CNN-based models.
• Score: 2.0305676256390934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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