Image compression has experienced a new revolution with the success of deep learning, which yields superior rate-distortion (RD) performance against traditional codecs. Yet, high computational complexity and energy consumption are the main bottlenecks that hinder the practical applicability of deep-learning-based (DL-based) codecs. Inspired by the neural network’s hierarchic structure yet with lower complexity, we propose a new lightweight image coding framework and name it the ”Green Image Codec” (GIC) in this work. First, GIC down-samples an input image into several spatial resolutions from fine-to-coarse grids and computes image residuals between two adjacent grids. Then, it encodes the coarsest content, interpolates content from coarse-tofine grids, encodes residuals, and adds residuals to interpolated images for reconstruction. All coding steps are implemented by vector quantization while all interpolation steps are conducted by the Lanczos interpolation. To facilitate VQ codebook training, the Saab transform is applied for energy compaction and, thus, dimension reduction. A simple rate-distortion optimization (RDO) is developed to help select the coding parameters. GIC yields an RD performance that is comparable with BPG at significantly lower complexity.
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.
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