Adaptive Image Compression Using GAN based Semantic-perceptual Residual Compensation

Image compression is a basic task in image processing. The existing methods always have problems such as the loss of image details and the reconstructed image does not conform to human vision. This paper presents an adaptive image compression algorithm that relies on GAN based semantic-perceptual residual compensation, which is available to offer visually pleasing reconstruction at a low bitrate. Our method derive from a U-shaped encoder-decoder structure accompanied by a well-designed dense residual connection with a strip pooling module to improve the original auto-encoder. Besides, we utilize the idea of adversarial learning by introducing a discriminator, thus constructed a complete GAN. To improve the coding efficiency, we creatively designed an adaptive semantic-perception residual compensation block based on the Grad-CAM algorithm. Through the strategy of adversarial learning, the reconstructed image is more towards the distribution of the real image, and further semantic perception can achieve higher quality compression of the region of interest from the human attention. Besides, we combine multiple existing quantitative methods, including the latest FLIF lossless compression algorithm, BPG vector compression algorithm and soft-quantization to perform deeper compression on the image. Experimental results, including PSNR, MS-SSIM demonstrate that the proposed approach outperforms the current state-of-the-art image compression methods.