GroupedMixer: An Efficient Transformer-based Entropy Model with Group-wise Token-Mixers for Learned Image Compression

Core Concepts

The proposed GroupedMixer is a novel transformer-based entropy model that employs group-wise autoregression and decomposes the global attention mechanism into more efficient inner-group and cross-group token-mixers, enabling faster coding speed and better compression performance compared to previous transformer-based methods.

Abstract

The paper introduces a novel transformer-based entropy model called GroupedMixer for learned image compression. The key highlights are: GroupedMixer partitions the latent variables into groups along spatial and channel dimensions, and then employs two group-wise token-mixers - inner-group and cross-group token-mixers - to integrate contextual information within each group and across previously decoded groups, respectively. The global causal self-attention is decomposed into more efficient group-wise interactions, which reduces the computational complexity compared to directly applying full self-attention. To further accelerate the network inference, the authors introduce context cache optimization to GroupedMixer, which caches attention activation values in cross-group token-mixers and avoids complex and duplicated computation. Experimental results demonstrate that the proposed GroupedMixer achieves state-of-the-art rate-distortion performance on standard benchmarks, while maintaining fast coding speed, outperforming previous transformer-based entropy models. The authors also analyze the insights behind the enhanced performance, increased coding speed, and reduced parameter count achieved by GroupedMixer compared to existing methods.

Stats

The paper reports the following key metrics: BD-Rate savings of GroupedMixer, GroupedMixer-Fast, and GroupedMixer-Large models on Kodak (17.84%, 6.03%, 17.81%), CLIC'21 Test (19.77%), and Tecnick (22.56%) datasets compared to VVC. Encoding and decoding latency for GroupedMixer and GroupedMixer-Fast models at different resolutions, ranging from 60-1800 ms. Total parameter counts for the proposed models, ranging from 36.4M to 72.8M.

Quotes

"Our approach builds upon group-wise autoregression by first partitioning the latent variables into groups along spatial-channel dimensions, and then entropy coding the groups with the proposed transformer-based entropy model." "We introduce context cache optimization to GroupedMixer, which caches attention activation values in cross-group token-mixers and avoids complex and duplicated computation." "Experimental results demonstrate that the proposed GroupedMixer yields the state-of-the-art rate-distortion performance with fast compression speed."

Key Insights Distilled From

GroupedMixer: An Entropy Model with Group-wise Token-Mixers for Learned Image Compression

by Daxin Li,Yua... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01170.pdf

Deeper Inquiries

How can the group-wise autoregression and token-mixer design in GroupedMixer be extended to other transformer-based tasks beyond image compression

The group-wise autoregression and token-mixer design in GroupedMixer can be extended to various transformer-based tasks beyond image compression by adapting the architecture to suit the specific requirements of the task at hand. Here are some potential extensions: Natural Language Processing (NLP): In NLP tasks such as language modeling or machine translation, the group-wise autoregression concept can be applied to process sequential data like sentences or paragraphs. Token-mixers can be used to capture dependencies within and across different parts of the input text, enhancing the model's ability to understand and generate coherent language. Time Series Forecasting: For tasks involving time series data like stock price prediction or weather forecasting, the group-wise autoregression can be utilized to capture temporal dependencies. Token-mixers can help in modeling interactions between different time steps or features, improving the model's forecasting accuracy. Audio Processing: In tasks related to audio analysis such as speech recognition or music generation, the group-wise autoregression can be adapted to process audio signals. Token-mixers can be employed to capture dependencies within different frequency bands or time segments, enabling the model to better understand and generate audio data. Video Processing: For video-related tasks like action recognition or video compression, the group-wise autoregression can be extended to handle spatio-temporal dependencies. Token-mixers can help in capturing interactions between different frames or regions in the video, enhancing the model's ability to analyze and process video data efficiently. By customizing the group-wise autoregression and token-mixer design to suit the specific characteristics of different tasks, the GroupedMixer architecture can be effectively applied to a wide range of transformer-based applications beyond image compression.

What are the potential limitations or drawbacks of the GroupedMixer approach, and how could they be addressed in future work

While GroupedMixer offers significant advantages in terms of compression performance and speed, there are some potential limitations and drawbacks that could be addressed in future work: Scalability: One limitation of GroupedMixer is its scalability to larger group sizes. As the number of groups increases, the computational complexity also grows, which can impact the model's efficiency. Future work could focus on optimizing the architecture to handle larger group sizes without compromising performance. Generalization: GroupedMixer may face challenges in generalizing to diverse datasets with varying characteristics. Fine-tuning the model on a wide range of data distributions and domains could help improve its generalization capabilities. Complexity: The design of the token-mixers and group-wise autoregression in GroupedMixer may introduce additional complexity to the model, which could affect interpretability and training stability. Future research could explore simplifying the architecture while maintaining its performance. Memory Efficiency: GroupedMixer may require significant memory resources, especially when processing high-resolution images or large datasets. Optimizing memory usage and exploring techniques like model pruning could help address this limitation. By addressing these potential drawbacks through further research and optimization, GroupedMixer can be enhanced to overcome its limitations and achieve even better performance in various applications.

Given the insights provided, how might the GroupedMixer architecture be further optimized or adapted to achieve even faster inference speeds without sacrificing compression performance

To achieve even faster inference speeds without sacrificing compression performance, the GroupedMixer architecture can be further optimized or adapted in the following ways: Efficient Attention Mechanisms: Implement more efficient attention mechanisms, such as sparse attention or approximate attention, to reduce the computational overhead of the self-attention process. These techniques can help speed up inference without compromising the model's ability to capture long-range dependencies. Quantization and Pruning: Apply quantization techniques to reduce the precision of model parameters and activations, leading to faster computations. Additionally, model pruning can be employed to remove redundant parameters and streamline the architecture, further improving inference speed. Parallel Processing: Utilize parallel processing techniques, such as model parallelism or data parallelism, to distribute computations across multiple devices or cores. This can significantly accelerate the inference process by leveraging parallel computing resources. Hardware Acceleration: Explore hardware acceleration options, such as using GPUs or specialized hardware like TPUs, to speed up the model's computations. Customizing the model for specific hardware architectures can lead to substantial improvements in inference speed. By implementing these optimization strategies and leveraging advancements in hardware and algorithm efficiency, the GroupedMixer architecture can be fine-tuned to achieve even faster inference speeds while maintaining high compression performance.