  Video content has experienced a surge in popularity, asserting its dominance
over internet traffic and Internet of Things (IoT) networks. Video compression
has long been regarded as the primary means of efficiently managing the
substantial multimedia traffic generated by video-capturing devices.
Nevertheless, video compression algorithms entail significant computational
demands in order to achieve substantial compression ratios. This complexity
presents a formidable challenge when implementing efficient video coding
standards in resource-constrained embedded systems, such as IoT edge node
cameras. To tackle this challenge, this paper introduces NU-Class Net, an
innovative deep-learning model designed to mitigate compression artifacts
stemming from lossy compression codecs. This enhancement significantly elevates
the perceptible quality of low-bit-rate videos. By employing the NU-Class Net,
the video encoder within the video-capturing node can reduce output quality,
thereby generating low-bit-rate videos and effectively curtailing both
computation and bandwidth requirements at the edge. On the decoder side, which
is typically less encumbered by resource limitations, NU-Class Net is applied
after the video decoder to compensate for artifacts and approximate the quality
of the original video. Experimental results affirm the efficacy of the proposed
model in enhancing the perceptible quality of videos, especially those streamed
at low bit rates.
