论文标题
智能手机相机照片上无参考图像质量评估的多任务深CNN模型
Multi-task deep CNN model for no-reference image quality assessment on smartphone camera photos
论文作者
论文摘要
智能手机是当今移动社交网络时代最成功的消费电子产品。智能手机相机质量及其图像后处理能力是影响消费者购买决策的主要因素。但是，从智能手机拍摄的照片的质量评估仍然是一项劳动密集型的工作，并依赖于专业摄影师和专家。作为先前基于CNN的NR-IQA方法的扩展，我们提出了一个以场景类型检测为辅助任务的多任务深CNN模型。借助卷积层中共享的模型参数，学习的特征图可能会变得更加相关并增强性能。评估结果表明，与传统的NR-IQA方法和基于CNN的单一任务模型相比，SROCC的性能提高了。
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.
