Jimin Chen, Zehao Lin. End-to-End Learning-Based Image Compression: A Review[J]. Laser & Optoelectronics Progress, 2020, 57(22): 220002
- Laser & Optoelectronics Progress
- Vol. 57, Issue 22, 220002 (2020)
Fig. 1. Technical roadmap of end-to-end learning-based image compression
![Nonlinear transform based end-to-end learning image compression[10]](/richHtml/lop/2020/57/22/220002/img_2.jpg)
Fig. 2. Nonlinear transform based end-to-end learning image compression[10]
![Images obtained by different quantization methods using JPEG compression[14]. (a) Original image; (b) rounding; (c) stochastic rounding; (d) additive noise](/Images/icon/loading.gif){kind=icon}
Fig. 3. Images obtained by different quantization methods using JPEG compression[14]. (a) Original image; (b) rounding; (c) stochastic rounding; (d) additive noise
Fig. 4. Autoencoder based on hyperprior and recursive nearest neighbor probability fusion[27]
Fig. 5. Objective evaluation. (a) Pixel-level distortion measured by MS-SSIM (dB); (b) PSNR used for structural similarity evaluation
Fig. 6. Subjective evaluation. (a) JPEG420; (b) BPG444; (c) NLAIC MSE opt; (d) NLAIC MS-SSIM opt;(e) original image
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