  The emergent ecosystems of intelligent edge devices in diverse Internet of
Things (IoT) applications, from automatic surveillance to precision
agriculture, increasingly rely on recording and processing variety of image
data. Due to resource constraints, e.g., energy and communication bandwidth
requirements, these applications require compressing the recorded images before
transmission. For these applications, image compression commonly requires: (1)
maintaining features for coarse-grain pattern recognition instead of the
high-level details for human perception due to machine-to-machine
communications; (2) high compression ratio that leads to improved energy and
transmission efficiency; (3) large dynamic range of compression and an easy
trade-off between compression factor and quality of reconstruction to
accommodate a wide diversity of IoT applications as well as their time-varying
energy/performance needs. To address these requirements, we propose, MAGIC, a
novel machine learning (ML) guided image compression framework that judiciously
sacrifices visual quality to achieve much higher compression when compared to
traditional techniques, while maintaining accuracy for coarse-grained vision
tasks. The central idea is to capture application-specific domain knowledge and
efficiently utilize it in achieving high compression. We demonstrate that the
MAGIC framework is configurable across a wide range of compression/quality and
is capable of compressing beyond the standard quality factor limits of both
JPEG 2000 and WebP. We perform experiments on representative IoT applications
using two vision datasets and show up to 42.65x compression at similar accuracy
with respect to the source. We highlight low variance in compression rate
across images using our technique as compared to JPEG 2000 and WebP.
