CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement
CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement
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DOI：
10.1109/tcsvt.2021.3123621
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发表时间：
2022-07
影响因子：
8.4
通讯作者：
Heng Zhao;Bolun Zheng;Shanxin Yuan;Hua Zhang;C. Yan;Liang Li;G. Slabaugh
中科院分区：
文献类型：
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作者：
Heng Zhao;Bolun Zheng;Shanxin Yuan;Hua Zhang;C. Yan;Liang Li;G. Slabaugh
恒定比特率（CBR）视频被广泛用于流媒体播放应用中。然而，CBR视频的图像质量往往是不稳定的，特别是对于具有大运动的场景。为此，我们设计了一个新的模型来表示高效率视频编码（HEVC）恒定比特率视频的失真，并提出了一种用于恒定比特率视频质量增强（CBREN）的神经网络。我们提出了一个双域恢复模块（DRM），共同学习的像素域和频域的先验知识。为了解决压缩造成的退化，我们提出了一个两步量化退化估计策略。逆DCT（IDCT）转换单元（ITU）用于将恒定比特率视频的量化表约束到合适的范围，动态阿尔法单元（DAU）用于根据每帧的内容微调量化表。为了有效地减少压缩过程中产生的不同大小的块失真，我们采用了多尺度网络。大量的实验表明，我们的方法可以大大提高CBR压缩视频的质量。此外，我们的方法也可以应用于恒定量化参数（CQP）的视频增强任务，当然是上级优于现有的方法。
Constant bit rate (CBR) videos are widely used in streaming playback applications. However, the image quality of the CBR video is often unstable, especially for scenes with large motion. To this end, we design a new model to represent the distortion of High Efficiency Video Coding (HEVC) constant bit rate video, and propose a neural network for a constant bit rate video quality enhancement (CBREN). We propose a dual-domain restoration module (DRM) to jointly learn the prior knowledge in the pixel domain and the frequency domain. To address the degradation resulting from compression, we propose a two-step quantization degradation estimation strategy. The Inverse DCT (IDCT) Translation Unit (ITU) is used to constrain the quantization table of the constant bit rate video to a suitable range, and the Dynamic Alpha Unit (DAU) is used to fine-tune the quantization table according to the content of each frame. In order to effectively reduce the block distortion of different sizes produced in the compression process, we adopt a multi-scale network. Extensive experiments show that our approach can greatly enhance the quality of CBR compressed video. Moreover, our method can also be applied to constant quantization parameter (CQP) video enhancement tasks, and is certainly superior to existing methods.