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.

Video compression is a crucial aspect of multimedia information systems, as it enables efficient management of the large amounts of data generated by video-capturing devices. However, traditional video compression algorithms require considerable computational power to achieve high compression ratios. This poses a significant challenge for resource-constrained embedded systems like IoT edge node cameras.

In this context, the introduction of NU-Class Net, an innovative deep-learning model, seeks to address this challenge by mitigating compression artifacts resulting from lossy compression codecs. By using NU-Class Net, the video encoder can reduce the output quality and generate low-bit-rate videos, thereby reducing computation and bandwidth requirements at the edge. On the decoder side, NU-Class Net compensates for the artifacts introduced during compression and approximates the quality of the original video.

The results of experimental analysis demonstrate the effectiveness of NU-Class Net in enhancing the perceptible quality of videos, particularly those streamed at low bit rates. The application of this deep-learning model opens up new possibilities for improving video quality while minimizing resource consumption in multimedia systems.

This research demonstrates the multi-disciplinary nature of multimedia information systems, showcasing the intersection of video compression algorithms, deep learning, and IoT edge computing. The development and application of NU-Class Net leverage advancements in artificial intelligence and neural networks to address the specific challenges faced by video encoding and decoding in resource-constrained environments.

Moreover, this research has significant implications for emerging technologies such as virtual reality (VR), augmented reality (AR), and artificial reality (AR). These immersive technologies heavily rely on high-quality video content to enhance user experiences. By improving video quality at low bit rates, NU-Class Net can contribute to more seamless and immersive VR, AR, and mixed reality experiences.

As video content continues to dominate internet traffic and IoT networks, the development of efficient video compression techniques is crucial. The introduction of NU-Class Net represents an important step forward in achieving high-quality video compression while minimizing computational demands and bandwidth requirements. With further research and development, this deep-learning model has the potential to revolutionize video encoding and decoding in multimedia systems and empower the widespread use of video content across various domains.
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