CNN-Based DCT-Like Transform for Image Compression

This paper presents a block transform for image compression, where the transform is inspired by discrete cosine transform (DCT) but achieved by training convolutional neural network (CNN) models. Specifically, we adopt the combination of convolution, nonlinear mapping, and linear transform to form a non-linear transform as well as a non-linear inverse transform. The transform, quantization, and inverse transform are jointly trained to achieve the overall rate-distortion optimization. For the training purpose, we propose to estimate the rate by the \(l_1\)-norm of the quantized coefficients. We also explore different combinations of linear/non-linear transform and inverse transform. Experimental results show that our proposed CNN-based transform achieves higher compression efficiency than fixed DCT, and also outperforms JPEG significantly at low bit rates.