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High Perceptual Image Compression Based on Conditional GAN
ZHANG Xue-feng， XU Hua-wen， YANG Mian-zimei
2022, 43 (6):
783-791.
DOI: 10.12068/j.issn.1005-3026.2022.06.004
A conditional generative adversarial network-based high perceptual image compression(HPIC) model was proposed to obtain a compressed image that conforms to human visual perception. In HPIC， the original image was encoded and quantized based on a hyper prior probability model. A generator based on the conditional additional label and residual module was used for the reconstruction of the compressed image， and a discriminator based on the convolutional neural network was used to distinguish the difference between the compressed image and the real image. Based on the bit rate-distortion-perception optimization theory to design the loss function， the perceptual distortion metric was chosen based on the eigenvalues of the pre-trained inception network to achieve image compression reconstruction with high perceptual quality on the one hand， and use the adversarial generation network loss to eliminate compression artifacts and improve the compression accuracy on the other hand. The experimental results show that HPIC achieves a good balance in the bit rate-distortion-perception tradeoff and outperforms the current common algorithms in all perceptual metrics scores， even though the latter uses twice as much bit rate as ours. HPIC can achieve compression with high perceptual quality.
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