MCUCoder: Adaptive bitrate learned video compression for IoT devices

The rapid growth of camera-based Internet of Things (IoT) devices demands the need for efficient video compression, particularly for edge applications where devices face hardware constraints, often with only 1 or 2 MB of RAM and unstable internet connections. Traditional and deep video compression methods are designed for high-end hardware, exceeding the capabilities of these constrained devices. Consequently, video compression in these scenarios is often limited to Motion-JPEG (M-JPEG) due to its high hardware efficiency and low complexity. This paper introduces MCUCoder, an open-source adaptive bitrate video compression model tailored for resource-limited IoT settings. MCUCoderfeatures an ultra-lightweight encoder with only 10.5K parameters and a minimal 350KB memory footprint, making it well-suited for edge devices and Microcontrollers (MCUs). While MCUCoderuses a similar amount of energy as M-JPEG, it reduces bitrate by 55.65% on the MCL-JCV dataset and 55.59% on the UVG dataset, measured in Multi-Scale Structural Similarity (MS-SSIM). Moreover, MCUCodersupports adaptive bitrate streaming by generating a latent representation that is sorted by importance, allowing transmission based on available bandwidth. This ensures smooth real-time video transmission even under fluctuating network conditions on low-resource devices. Source code available at https://github.com/ds-kiel/MCUCoder.

DDC Class

621.3: Electrical Engineering, Electronic Engineering

004: Computer Sciences

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https://creativecommons.org/licenses/by-nc-nd/4.0/

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submittedVersion

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2411.19442v1.pdf

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6.71 MB

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MCUCoder: Adaptive bitrate learned video compression for IoT devices