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.
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