  Learning-based image compression was shown to achieve a competitive
performance with state-of-the-art transform-based codecs. This motivated the
development of new learning-based visual compression standards such as JPEG-AI.
Of particular interest to these emerging standards is the development of
learning-based image compression systems targeting both humans and machines.
This paper is concerned with learning-based compression schemes whose
compressed-domain representations can be utilized to perform visual processing
and computer vision tasks directly in the compressed domain. Such a
characteristic has been incorporated as part of the scope and requirements of
the new emerging JPEG-AI standard. In our work, we adopt the learning-based
JPEG-AI framework for performing material and texture recognition using the
compressed-domain latent representation at varing bit-rates. For comparison,
performance results are presented using compressed but fully decoded images in
the pixel domain as well as original uncompressed images. The obtained
performance results show that even though decoded images can degrade the
classification performance of the model trained with original images,
retraining the model with decoded images will largely reduce the performance
gap for the adopted texture dataset. It is also shown that the
compressed-domain classification can yield a competitive performance in terms
of Top-1 and Top-5 accuracy while using a smaller reduced-complexity
classification model.
