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Compression for machines is an emerging field, where inputs are encoded whileoptimizing the performance of downstream automated analysis. In scalable codingfor humans and machines, the compressed representation used for machines isfurther utilized to enable input reconstruction. Often performed by jointlyoptimizing the compression scheme for both machine task and human perception,this results in sub-optimal rate-distortion (RD) performance for the machineside. We focus on the case of images, proposing to utilize the pre-existingresidual coding capabilities of video codecs such as VVC to create a scalablecodec from any image compression for machines (ICM) scheme. Using our approachwe improve an existing scalable codec to achieve superior RD performance on themachine task, while remaining competitive for human perception. Moreover, ourapproach can be trained post-hoc for any given ICM scheme, and without creatinga coupling between the quality of the machine analysis and human vision.

VVC+M: Plug and Play Scalable Image Coding for Humans and Machines