Image encoding technology has long been one of the most fundamental technologies for multimedia device transmission and storage. In the past, image encoding techniques primarily evolved to cater to human visual perception. However, as the amount of image data has significantly increased, most images no longer need to be viewed by humans but are processed by computers and other intelligent devices. This shift has created a need for image encoding technologies that serve both human visual perception and computer vision. Consequently, in recent years, many researchers have designed various human-machine-friendly scalable image encoding schemes to meet these requirements. Nevertheless, the number of human-machine-friendly scalable image encoding schemes remains relatively limited. This paper presents a human-computer-friendly scalable image coding scheme based on Canny edge identification technique. Firstly, Canny edge identification technique is used to accurately and comprehensively extract image edges and details. Feature analysis is then applied, followed by a generative model that reconstructs the image using the extracted characteristics and added reference pixels. Under this scheme, the closely packed edge map establishes a scalable connection between human and machine vision: the closely packed edge map serves the role of the foundational layer for machine-oriented vision tasks, while the fiducial pixels serve the role of an augmentation layer to ensure signal quality for human perception. Through incorporating a Sophisticated generative model, we trained a flexible network to rebuild the image from the closely packed feature representation and reference pixels. Finally, numerous experiments demonstrate the presented framework is superior to the Sophisticated approach in both human visual perception and computer vision tasks.