  With the AI of Things (AIoT) development, a huge amount of visual data, e.g.,
images and videos, are produced in our daily work and life. These visual data
are not only used for human viewing or understanding but also for machine
analysis or decision-making, e.g., intelligent surveillance, automated
vehicles, and many other smart city applications. To this end, a new image
codec paradigm for both human and machine uses is proposed in this work.
Firstly, the high-level instance segmentation map and the low-level signal
features are extracted with neural networks. Then, the instance segmentation
map is further represented as a profile with the proposed 16-bit gray-scale
representation. After that, both 16-bit gray-scale profile and signal features
are encoded with a lossless codec. Meanwhile, an image predictor is designed
and trained to achieve the general-quality image reconstruction with the 16-bit
gray-scale profile and signal features. Finally, the residual map between the
original image and the predicted one is compressed with a lossy codec, used for
high-quality image reconstruction. With such designs, on the one hand, we can
achieve scalable image compression to meet the requirements of different human
consumption; on the other hand, we can directly achieve several machine vision
tasks at the decoder side with the decoded 16-bit gray-scale profile, e.g.,
object classification, detection, and segmentation. Experimental results show
that the proposed codec achieves comparable results as most learning-based
codecs and outperforms the traditional codecs (e.g., BPG and JPEG2000) in terms
of PSNR and MS-SSIM for image reconstruction. At the same time, it outperforms
the existing codecs in terms of the mAP for object detection and segmentation.
