부산대학교 도서관

Recently, compression is essential to reduce large traffic generated from video or image-based services such as OTT, IOT, smart city, and self-driving cars. The high data compression reduces data traffic, but causes information loss and data quality deterioration. In this paper, we shall propose a new efficient compression method which increases the compression ratio while maintaining the image quality by partitioning the image into 5 different areas each with the different quantization values according to their priority such as intensity, location, size and correlation. We exploit different compression ratio for each of the areas divided based on saliency map according to their visual priority order in order to fully reflect the visual reaction degree of human visual system. We shall show that our method dramatically increases data preservation and compression efficiency by compressing each area with the different quantization values while using the less memory.