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Inter-frame video image generation based on spatial continuity generative adversarial networks

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Abstract

This paper proposes a method for generating inter-frame video images based on spatial continuity generative adversarial networks (SC-GANs) to smooth the playing of low-frame rate videos and to clarify blurry image edges caused by the use of traditional methods to improve the video frame rate. Firstly, the auto-encoder is used as a discriminator and Wasserstein distance is applied to represent the difference between the loss distribution of the real sample and the generated sample, instead of the typical method of generative adversarial networks to directly match data distribution. Secondly, the hyperparameter between generator and discriminator is used to stabilize the training process, which effectively prevents the model from collapsing. Finally, taking advantage of the spatial continuity of the image features of continuous video frames, an optimal value between two consecutive frames is found by Adam and then mapped to the image space to generate inter-frame images. In order to illustrate the authenticity of the generated inter-frame images, PSNR and SSIM are adopted to evaluate the inter-frame images, and the results show that the generated inter-frame images have a high degree of authenticity. The feasibility and validity of the proposed method based on SC-GAN are also verified.

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Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, China

    Tao Zhang, Peipei Jiang & Meng Zhang

  2. Neusoft Software Co., Ltd., Qinhuangdao, Hebei, China

    Meng Zhang

Authors
  1. Tao Zhang
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  2. Peipei Jiang
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  3. Meng Zhang
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Correspondence to Tao Zhang.

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Zhang, T., Jiang, P. & Zhang, M. Inter-frame video image generation based on spatial continuity generative adversarial networks. SIViP 13, 1487–1494 (2019). https://doi.org/10.1007/s11760-019-01499-0

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  • DOI: https://doi.org/10.1007/s11760-019-01499-0

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