As an important subtask of video restoration, video super-resolution has attracted a lot of attention in the community as it can eventually promote a wide range of technologies, e.g., video transmission system. Recent video super resolution model1 achieves cutting-edge performance. It efficiently utilizes recurrent architecture with neural networks to gradually aggregate details from previous frames. Nevertheless, this method faces a serious drawback that it is sensitive to occlusion, blur, and large motion changes since it only takes the previous generated output as recurrent input for the super resolution model. This will lead to undesirable rapid information loss during the recurrently generating process, and performance will therefore be dramatically decreased. Our works focus on addressing the issue of rapid information loss in video super resolution model with recurrent architecture. By producing attention maps through selective fusion module, the recurrent model can adaptively aggregate necessary details across all previously generated high-resolution (HR) frames according to their informativeness. The proposed method is useful for preserving high frequency details collected progressively from each frame while being capable of removing noisy artifacts. This significantly improves the average quality of the super resolution video.
KEYWORDS: Super resolution, Quantization, Video, Video coding, Image quality, Video compression, Video processing, Machine vision, Computer vision technology, Signal processing
In video transmission, the videos are encoded and decoded. At that time, bit control is performed by specifying the quantization parameter (QP). The video undergoes various processing to remove redundancy and then orthogonally transforms the video signal into the frequency domain. The frequency domain coefficients are then quantized and transmitted. At that time, by specifying QP, the quantization step is changed, and the amount of data can be changed. In an opinion, a codec using super-resolution is proposed. At the CNN based super-resolution of encoded images, the degradation of the input image due to encoding depends on the characteristics of the image. As a result, there is a problem that the weights of the optimal CNN for the input image changes depending on the image characteristics. In order to solve this problem, we propose a method to adaptively perform super-resolution corresponding to image degradation.
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