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.

翻译：视频内容已迎来蓬勃发展，其主导地位体现在互联网流量及物联网网络中。视频压缩长期以来被视为有效管理视频采集设备产生的大量多媒体流量的主要手段。然而，视频压缩算法需要巨大的计算开销才能实现高压缩比。这一复杂性给在资源受限的嵌入式系统（如物联网边缘节点摄像头）中实现高效视频编码标准带来了严峻挑战。为应对该挑战，本文提出了NU-Class Net——一种创新的深度学习模型，旨在减轻由有损压缩编解码器产生的伪影。该增强显著提升了低比特率视频的可感知质量。通过采用NU-Class Net，视频采集节点中的编码器可降低输出质量，从而生成低比特率视频，并有效削减边缘端的计算与带宽需求。在通常不受资源限制的解码端，NU-Class Net在视频解码器后应用，以补偿伪影并接近原始视频的质量。实验结果证实了所提模型在提升视频（尤其是低比特率流媒体视频）可感知质量方面的有效性。