  Image compression, as one of the fundamental low-level image processing
tasks, is very essential for computer vision. Tremendous computing and storage
resources can be preserved with a trivial amount of visual information.
Conventional image compression methods tend to obtain compressed images by
minimizing their appearance discrepancy with the corresponding original images,
but pay little attention to their efficacy in downstream perception tasks,
e.g., image recognition and object detection. Thus, some of compressed images
could be recognized with bias. In contrast, this paper aims to produce
compressed images by pursuing both appearance and perceptual consistency. Based
on the encoder-decoder framework, we propose using a pre-trained CNN to extract
features of the original and compressed images, and making them similar. Thus
the compressed images are discernible to subsequent tasks, and we name our
method as Discernible Image Compression (DIC). In addition, the maximum mean
discrepancy (MMD) is employed to minimize the difference between feature
distributions. The resulting compression network can generate images with high
image quality and preserve the consistent perception in the feature domain, so
that these images can be well recognized by pre-trained machine learning
models. Experiments on benchmarks demonstrate that images compressed by using
the proposed method can also be well recognized by subsequent visual
recognition and detection models. For instance, the mAP value of compressed
images by DIC is about 0.6% higher than that of using compressed images by
conventional methods.
