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Reduced Complexity Superresolution for Low-Bitrate Video Compression

机译:降低复杂度的超分辨率,实现低比特率视频压缩

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摘要

Evolving video applications impose requirements for high image quality, low bitrate, and/or small computational cost. This paper combines state-of-the-art coding and superresolution (SR) techniques to improve video compression both in terms of coding efficiency and complexity. The proposed approach improves a generic decimation–quantization compression scheme by introducing low complexity single-image SR techniques for rescaling the data at the decoder side and by jointly exploring/optimizing the downsampling/upsampling processes. The enhanced scheme achieves improvement of the quality and system’s complexity compared with conventional codecs and can be easily modified to meet various diverse requirements, such as effectively supporting any off-the-shelf video codec, for instance H.264/Advanced Video Coding or High Efficiency Video Coding. Our approach builds on studying the generic scheme’s parameterization with common rescaling techniques to achieve 2.4-dB peak signal-to-noise ratio (PSNR) quality improvement at low-bitrates compared with the conventional codecs and proposes a novel SR algorithm to advance the critical bitrate at the level of 10 Mb/s. The evaluation of the SR algorithm includes the comparison of its performance to other image rescaling solutions of the literature. The results show quality improvement by 5-dB PSNR over straightforward interpolation techniques and computational time reduction by three orders of magnitude when compared with the highly involved methods of the field. Therefore, our algorithm proves to be most suitable for use in reduced complexity downsampled compression schemes.
机译:不断发展的视频应用对高图像质量,低比特率和/或小的计算成本提出了要求。本文结合了最新的编码和超分辨率(SR)技术,从编码效率和复杂性两方面提高了视频压缩率。通过引入低复杂度单图像SR技术在解码器端重新缩放数据并联合探索/优化下采样/上采样过程,所提出的方法改进了通用的抽取量化量化方案。与传统的编解码器相比,增强的方案可以提高质量和系统的复杂性,并且可以轻松进行修改以满足各种不同的要求,例如有效地支持任何现成的视频编解码器,例如H.264 / Advanced Video Coding或High效率视频编码。我们的方法建立在研究通用方案的参数化以及常用的重新缩放技术的基础上,以实现与传统编解码器相比低比特率下2.4 dB的峰值信噪比(PSNR)质量改善,并提出了一种新颖的SR算法来提高关键比特率速度为10 Mb / s。 SR算法的评估包括将其性能与文献中其他图像缩放解决方案进行比较。结果表明,与直接参与的插值技术相比,质量提高了5 dB PSNR,与本领域高度参与的方法相比,计算时间减少了三个数量级。因此,我们的算法被证明最适合用于降低复杂度的下采样压缩方案。

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