Target-Dependent Scalable Image Compression Using a Reconfigurable Recurrent Neural Network

Conventional human-centric image compression techniques are optimized for human visual perception, and are generally evaluated by metrics such as MSSSIM and PSNR. On the other hand, task-centric image compression techniques that target deep neural networks (DNN) inference focus on understanding images, and are measured by inference accuracy. As images should be encoded and decoded differently depending on the target metric, existing deep learning based compression techniques have focused on designing an independent neural network that is optimized for either one of the two targets. However, as these techniques require the target metric to be determined during a training phase, separate DNN models should be trained for different target applications, allowing no scalability between human- and task-centric compression. In this paper, we propose a target-dependent scalable image compression pipeline, where compressed images can be differently decoded in real time depending on the interpolated target between perceptual quality and DNN inference accuracy. This dynamic scalability is supported by a reconfigurable recurrent neural network (RNN) that can dynamically change its flow according to the interpolation parameter. The experimental results show that the proposed method achieves comparable MSSSIM and classification accuracy to the compression networks individually trained for human-centric and task-centric targets, respectively, while providing scalability between the two targets depending on the interpolation parameter.