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