Learned Image Compression with Frequency Domain Loss

This paper proposes an end-to-end deep image compression model with a frequency domain loss function. Unlike previous deep image compression methods, the model is computed jointly in the frequency domain. By calculating in the frequency domain, the model incorporates high-frequency components to capture detailed information in the reconstructed images effectively. The process of frequency domain relates to the compression technologies, a concept universal to modern im- age/video codecs (e.g., JPEG), but it has seldom been investigated in a deep image compression model based on neural networks. It was demonstrated that this model shows better image compression performance when measuring visual quality using the peak signal-to-noise ratio, and its rate-distortion performance outperformed traditional neural-network-based models when the model was trained jointly in the frequency domain. This model improves the performance of image compression, especially when the bitrate was low. Moreover, the method can be used and applicable to other compression models easily.