A Novel Image Compression Framework at Edges

For the new developed area of edge computing, the traditional image coding schemes always have poor adaptability on the requirements of high-compression and low-delay coding, due to the fact that they do not consider the correlations between encoding image and external images. Aiming at this problem, we propose a novel image coding framework based on the content similarity analysis. Unlike the current state-of-the-art systems, our method compresses all the images stored in the edge computing terminal as a whole. The images are represented by their pHash values and classified into many groups, then each group is compressed in a quasi inter-prediction coding way. With our coding scheme, the redundancies among the similar images could be removed to achieve more coding gain. Experimental results demonstrate the superiorities of our method in the edge computing environment compared with current static image coding solutions.