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：视频内容已迎来普及热潮，在互联网流量和物联网（IoT）网络中占据主导地位。长期以来，视频压缩被视为有效管理视频采集设备产生的大规模多媒体流量的主要手段。然而，视频压缩算法为实现高压缩比需要巨大的计算开销。这种复杂性对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构成了严峻挑战。为应对这一挑战，本文提出NU-Class Net——一种创新的深度学习模型，旨在减轻有损压缩编解码器产生的压缩伪影。该模型显著提升了低码率视频的感知质量。通过采用NU-Class Net，视频采集节点内的视频编码器可降低输出质量，从而生成低码率视频，并有效减少边缘端的计算与带宽需求。在通常受资源限制较小的解码端，NU-Class Net被应用于视频解码器之后，以补偿伪影并逼近原始视频质量。实验结果证实，所提模型在提升视频（尤其是低码率流媒体视频）的感知质量方面具有显著效果。