CNN-based super-resolution adapted to quantization parameters

Toshiya Hori; Zichen Gong; Hiroshi Watanabe; Tomohiro Ikai; Takeshi Chujoh; Eiichi Sasaki; Norio Ito

doi:10.1117/12.2566911

1 June 2020 CNN-based super-resolution adapted to quantization parameters

Toshiya Hori, Zichen Gong, Hiroshi Watanabe, Tomohiro Ikai, Takeshi Chujoh, Eiichi Sasaki, Norio Ito

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115151U (2020) https://doi.org/10.1117/12.2566911
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

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.

Citation Download Citation

Toshiya Hori, Zichen Gong, Hiroshi Watanabe, Tomohiro Ikai, Takeshi Chujoh, Eiichi Sasaki, and Norio Ito "CNN-based super-resolution adapted to quantization parameters", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115151U (1 June 2020); https://doi.org/10.1117/12.2566911

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