Content-Adaptive Tokenizer (CAT): Image Tokenizer That Adapts Token Counting Based on Image Complexity, Provides Flexible 8x, 16x, or 32x Compression

One of the biggest obstacles to AI-driven image visualization is the inability to effectively account for variations in image content. The tokenization methods used so far are static compression ratios where all images are treated equally, and the complexities of the images are not considered. For this reason, complex images are overcompressed and lead to the loss of important information, while simple images remain compressed, wasting valuable computational resources. This inefficiency hinders the performance of subsequent tasks such as reconstruction and image reproduction, where accurate representation and efficiency play an important role.

Current methods of image tokenization do not account for the difficulty variable properly. A fixed-rate token approaches resizing images to standard sizes without considering content variations. Vision Transformers adjust patch size dynamically but are dependent on image input and lack the flexibility of text-to-image applications. Other compression techniques include JPEG, which is designed specifically for traditional media but lacks deep learning-based tokenization capabilities. The current work, ElasticTok, provided random token length strategies but did not consider the complexity of the underlying content during training; this leads to inefficiencies in terms of quality and associated computational costs.

Researchers from Carnegie Mellon University and Meta propose Content-Adaptive Tokenization (CAT), a pioneering framework for content-aware image tokenization that introduces an adaptive approach by assigning representation capacity based on content complexity. This innovation enables large-scale language models to assess the complexity of images from captions and perception-based questions while splitting images into three compression levels: 8x, 16x, and 32x. In addition, it uses a nested VAE structure that generates hidden features of variable length by dynamically moving average results based on the complexity of the images. The adaptive design reduces overhead training and improves the quality of image representation to overcome the inefficiency of fixed rate methods. CAT enables flexible and efficient tokenization using complex text-based analysis without requiring graphical input for understanding.

CAT evaluates the complexity and captions produced in LLMs that consider both semantic, visual, and visual aspects while determining compression ratios. Such a caption-based system appears to be superior to traditional methods, including JPEG size and MSE in its ability to simulate human perceived value. This flexible VAE design does so with a channel-matched jump connection that dynamically changes the latent space across various pressure levels. Shared parameterization ensures consistency at all scales, while training is performed with a combination of reconstruction error, vision loss (for example, LPIPS), and resistance loss to achieve optimal performance. CAT was trained on a dataset of 380 million images and tested on COCO, ImageNet, CelebA, and ChartQA benchmarks, thereby demonstrating its performance on different image types.

This achieves significant performance improvements in both image reconstruction and production by adjusting compression based on content complexity. With reconstruction functions, it significantly improves the rFID, LPIPS, and PSNR metrics. It delivers a 12% quality improvement for CelebA reconstruction and a 39% improvement for ChartQA, all while maintaining quality comparable to datasets like COCO and ImageNet with fewer tokens and efficiency. In the classification-based ImageNet implementation, CAT outperforms the fixed-rate baselines with an FID of 4.56 and improves inference by 18.5%. This flexible token framework is a new sign of further development.

CAT is a new method for image tokenization by dynamically changing compression levels based on the complexity of the content. It combines LLM-based evaluation with nested variable VAE, eliminating persistent inefficiencies associated with fixed-rate tokens, thereby greatly improving performance in reengineering and manufacturing operations. CAT's adaptability and efficiency make it a versatile asset in AI-centric imaging, with potential applications extending into video and multi-modal domains.

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Aswin AK is a consultant at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, which brings a strong academic background and practical experience in solving real-life domain challenges.