  Conventional cameras capture image irradiance on a sensor and convert it to
RGB images using an image signal processor (ISP). The images can then be used
for photography or visual computing tasks in a variety of applications, such as
public safety surveillance and autonomous driving. One can argue that since RAW
images contain all the captured information, the conversion of RAW to RGB using
an ISP is not necessary for visual computing. In this paper, we propose a novel
$\rho$-Vision framework to perform high-level semantic understanding and
low-level compression using RAW images without the ISP subsystem used for
decades. Considering the scarcity of available RAW image datasets, we first
develop an unpaired CycleR2R network based on unsupervised CycleGAN to train
modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB
images. We can then flexibly generate simulated RAW images (simRAW) using any
existing RGB image dataset and finetune different models originally trained for
the RGB domain to process real-world camera RAW images. We demonstrate object
detection and image compression capabilities in RAW-domain using RAW-domain
YOLOv3 and RAW image compressor (RIC) on snapshots from various cameras.
Quantitative results reveal that RAW-domain task inference provides better
detection accuracy and compression compared to RGB-domain processing.
Furthermore, the proposed \r{ho}-Vision generalizes across various camera
sensors and different task-specific models. Additional advantages of the
proposed $\rho$-Vision that eliminates the ISP are the potential reductions in
computations and processing times.
