  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.
