Variable Rate Deep Image Compression With a Conditional Autoencoder (1909.04802v1)

Abstract: In this paper, we propose a novel variable-rate learned image compression framework with a conditional autoencoder. Previous learning-based image compression methods mostly require training separate networks for different compression rates so they can yield compressed images of varying quality. In contrast, we train and deploy only one variable-rate image compression network implemented with a conditional autoencoder. We provide two rate control parameters, i.e., the Lagrange multiplier and the quantization bin size, which are given as conditioning variables to the network. Coarse rate adaptation to a target is performed by changing the Lagrange multiplier, while the rate can be further fine-tuned by adjusting the bin size used in quantizing the encoded representation. Our experimental results show that the proposed scheme provides a better rate-distortion trade-off than the traditional variable-rate image compression codecs such as JPEG2000 and BPG. Our model also shows comparable and sometimes better performance than the state-of-the-art learned image compression models that deploy multiple networks trained for varying rates.

Variable Rate Deep Image Compression With a Conditional Autoencoder

The paper, "Variable Rate Deep Image Compression With a Conditional Autoencoder," introduces a novel approach to image compression using a variable-rate deep learning framework. Traditional image compression techniques, such as JPEG2000 and BPG, rely on fixed codecs and separate networks for different compression rates. By contrast, this paper proposes a single adaptable network that leverages a conditional autoencoder conditioned on two parameters: the Lagrange multiplier and the quantization bin size, to achieve variable rates of image compression.

Key Contributions and Methodology

1. Conditional Autoencoder: The paper's primary contribution is the implementation of a conditional autoencoder that adjusts the trade-off between rate and distortion by conditioning on the Lagrange multiplier, a hyper-parameter in optimization problems. This contrasts with the typical practice of training multiple separate networks each optimized for a distinct quality-rate setting.
2. Rate-Control Mechanisms:
   • Lagrange Multiplier ($\lambda$): Coarse adjustments to the rate are achieved by varying the Lagrange multiplier, allowing users to specify a preferred quality level.
   • Quantization Bin Size ($\Delta$): For fine tuning, adjusting the quantization bin size allows further control over the compression rate, optimizing the network performance across more granular, continuous adjustments.
3. Training Approach: The model is trained on mixed quantization bin sizes simultaneously with multiple Lagrange multiplier values, enabling the network's robustness across a spectrum of compression rates without retraining.
4. Universal Quantization: The paper employs universal quantization to relax optimization constraints during training. This method more effectively approximates the rate-distortion trade-off, thereby enhancing fidelity against varying quantization noise levels.
5. Probabilistic Model Refinement: The authors incorporate a hierarchical model with autoregressive probability models for latent variables. This includes a secondary latent variable for entropy coding, leading to improved entropy estimates and, thus, more efficient compression.

Experimental Evaluation

The researchers evaluated their model using the Kodak image dataset and compared its performance against classic and state-of-the-art methods, like BPG and other deep learning models. Experimental results demonstrated that the conditional autoencoder competes favorably, outperforming BPG in both PSNR and MS-SSIM metrics. Notably, the model achieved these results with a single trained network, in contrast to previous methods requiring multiple networks across different rates.

Implications and Future Directions

• Practical Benefits: The proposed method ameliorates the inefficiencies in training multiple networks for different compression rates. It's poised to improve real-world applications where storage constraints and bandwidth vary significantly across tasks and environments.
• Theoretical Insights: The framework opens avenues for exploring the application of conditional networks in solving other optimization problems where the method of Lagrange multipliers is relevant.
• Potential Extensions: Future work could involve extending the flexible conditional approach to video compression tasks or integrating it with perceptual metrics beyond MS-SSIM, aiming at end-to-end optimized visual fidelity.

This paper provides a significant advancement in learned image compression, presenting a versatile and efficient solution that bridges the gap between fixed-rate codecs and the flexibility required for real-world applications. The introduction of conditional autoencoders as a tool for variable-rate compression highlights the potential of machine learning to dynamically adapt to complex data-driven problems.