2020/08/27 by Chen-Hsiu Huang, Huang, Chen-Hsiu, Ja-Ling Wu +1
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Multimedia (cs.MM) #cs.CV #cs.MM
paper · pdf · doi:10.48550/arxiv.2008.11961
Proceedings of Computer Vision & Graphic Image Processing (CVGIP), Hsinchu, Taiwan, Aug. 16-18, 2020
arxiv created 2020/08/27 · arxiv updated 2020/08/28
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.