CV-Cast: Computer Vision-Oriented Linear Coding and Transmission

Abstract

Remote inference allows lightweight edge devices, such as autonomous drones, to perform vision tasks exceeding their computational, energy, or processing delay budget. In such applications, reliable transmission of information is challenging due to high variations of channel quality. Traditional approaches involving spatio-temporal transforms, quantization, and entropy coding followed by digital transmission may be affected by a sudden decrease in quality (the digital cliff) when the channel quality is less than expected during design. This problem can be addressed by using Linear Coding and Transmission (LCT), a joint source and channel coding scheme relying on linear operators only, allowing to achieve reconstructed per-pixel error commensurate with the wireless channel quality. In this paper, we propose CV-Cast: The first LCT scheme optimized for computer vision task accuracy instead of per-pixel distortion. Using this approach, for instance at 10 dB channel signal-to-noise ratio, CV-Cast requires transmitting 28% less symbols than a baseline LCT scheme in semantic segmentation and 15% in object detection tasks. Simulations involving a realistic 5G channel model confirm the smooth decrease in accuracy achieved with CV-Cast, while images encoded by JPEG or learned image coding (LIC) and transmitted using classical schemes at low Eb/N0 are subject to digital cliff.