DCST: a data-driven color/spatial transform-based image coding method

Block-based discrete cosine transform (DCT) and quantization matrices on YCbCr color channels play key roles in JPEG and have been widely used other standards in last three decades. In this work, we propose a new image coding method, called DCST. It adopts data-driven color transform and spatial transform based on statistical properties of pixels and machine learning. To match with the data-driven forward transform, we propose a method to design the quantization table based on human visual system (HVS). Furthermore, to efficiently compensate the quantization error, a machine learning based optimal inverse transform is proposed. Performance of our new design is verified using Kodak image dataset based on libjpeg. Our pipeline outperforms JPEG with a gain of 0.5738 in BD-PSNR (or a reduction of 9.5713 in BD-rate) range from 0.2 to 3bpp.