The paper "Joint Autoregressive and Hierarchical Priors for Learned Image Compression" by David Minnen, Johannes Ballé, and George Toderici explores advanced methods for image compression using learned models. The authors build upon existing work that uses autoencoders to learn invertible mappings from pixels to quantized latent representations, combined with entropy models for compression. They introduce autoregressive and hierarchical priors to exploit more structure in the latents, improving compression performance while maintaining end-to-end optimization. The key contributions of the paper include: 1. **Generalization of the Hierarchical Gaussian Scale Mixture (GSM) Model**: The authors extend the hierarchical GSM model to a Gaussian mixture model (GMM), allowing for more flexible and accurate modeling of the entropy distribution. 2. **Integration of Autoregressive Components**: Inspired by successful autoregressive priors in generative models, they incorporate autoregressive components into the hierarchical model to predict latents based on their causal context. 3. **Comprehensive Evaluation**: The paper evaluates the proposed models using both peak signal-to-noise ratio (PSNR) and multiscale structural similarity (MS-SSIM) metrics on the Kodak image set, demonstrating superior performance compared to existing methods. The authors find that while autoregressive models have computational overhead, they complement the hierarchical priors in terms of compression performance. The combined model achieves state-of-the-art rate-distortion performance, reducing file size by 15.8% on average compared to the previous best method based on deep learning. This reduction is significant, outperforming JPEG, WebP, JPEG2000, and BPG (the current state-of-the-art image codec) in various metrics. The paper also discusses the architectural details of the models, including the use of masked convolutions and the constraints on network layers to ensure that the compressed bitstream alone is sufficient for reconstruction. The experimental results show that the combined model outperforms all baseline methods, both in terms of PSNR and MS-SSIM, providing the highest visual quality at similar bit rates. Overall, the paper advances the field of learned image compression by demonstrating the effectiveness of combining autoregressive and hierarchical priors, achieving significant improvements in compression performance and visual quality.The paper "Joint Autoregressive and Hierarchical Priors for Learned Image Compression" by David Minnen, Johannes Ballé, and George Toderici explores advanced methods for image compression using learned models. The authors build upon existing work that uses autoencoders to learn invertible mappings from pixels to quantized latent representations, combined with entropy models for compression. They introduce autoregressive and hierarchical priors to exploit more structure in the latents, improving compression performance while maintaining end-to-end optimization. The key contributions of the paper include: 1. **Generalization of the Hierarchical Gaussian Scale Mixture (GSM) Model**: The authors extend the hierarchical GSM model to a Gaussian mixture model (GMM), allowing for more flexible and accurate modeling of the entropy distribution. 2. **Integration of Autoregressive Components**: Inspired by successful autoregressive priors in generative models, they incorporate autoregressive components into the hierarchical model to predict latents based on their causal context. 3. **Comprehensive Evaluation**: The paper evaluates the proposed models using both peak signal-to-noise ratio (PSNR) and multiscale structural similarity (MS-SSIM) metrics on the Kodak image set, demonstrating superior performance compared to existing methods. The authors find that while autoregressive models have computational overhead, they complement the hierarchical priors in terms of compression performance. The combined model achieves state-of-the-art rate-distortion performance, reducing file size by 15.8% on average compared to the previous best method based on deep learning. This reduction is significant, outperforming JPEG, WebP, JPEG2000, and BPG (the current state-of-the-art image codec) in various metrics. The paper also discusses the architectural details of the models, including the use of masked convolutions and the constraints on network layers to ensure that the compressed bitstream alone is sufficient for reconstruction. The experimental results show that the combined model outperforms all baseline methods, both in terms of PSNR and MS-SSIM, providing the highest visual quality at similar bit rates. Overall, the paper advances the field of learned image compression by demonstrating the effectiveness of combining autoregressive and hierarchical priors, achieving significant improvements in compression performance and visual quality.