Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need (2411.12448v2)

Abstract: We have recently witnessed that Intelligence" and Compression" are the two sides of the same coin, where the language large model (LLM) with unprecedented intelligence is a general-purpose lossless compressor for various data modalities. This attribute particularly appeals to the lossless image compression community, given the increasing need to compress high-resolution images in the current streaming media era. Consequently, a spontaneous envision emerges: Can the compression performance of the LLM elevate lossless image compression to new heights? However, our findings indicate that the naive application of LLM-based lossless image compressors suffers from a considerable performance gap compared with existing state-of-the-art (SOTA) codecs on common benchmark datasets. In light of this, we are dedicated to fulfilling the unprecedented intelligence (compression) capacity of the LLM for lossless image compression tasks, thereby bridging the gap between theoretical and practical compression performance. Specifically, we propose P$^{2}$-LLM, a next-pixel prediction-based LLM, which integrates various elaborated insights and methodologies, \textit{e.g.,} pixel-level priors, the in-context ability of LLM, and a pixel-level semantic preservation strategy, to enhance the understanding capacity of pixel sequences for better next-pixel predictions. Extensive experiments on benchmark datasets demonstrate that P$^{2}$-LLM can beat SOTA classical and learned codecs.

• The paper introduces P2-LLM, a language model that predicts pixel sequences to achieve lossless image compression, outperforming traditional methods.
• It integrates pixel-level priors and a two-step tokenization strategy to preserve semantic details and optimize coding efficiency.
• Fine-tuning through LoRA and predictive distribution sampling enable superior compression rates across diverse datasets.

LLMs for Lossless Image Compression: An Analysis of Next-Pixel Prediction Approaches

The intersection between machine intelligence and data compression is far from being novel. However, the approach taken by the paper "LLMs for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need" offers a refreshing perspective by leveraging LLMs for image compression. This discourse explicates the insights, methodologies, and results presented in the paper while considering the implications for both theoretical advancements and practical applications in lossless image compression.

The authors propose the P$^2$-LLM, a next-pixel prediction-based LLM designed specifically for lossless image compression. The P$^2$-LLM differentiates itself from traditional state-of-the-art (SOTA) coding techniques by employing an LLM to exploit its capacity to predict image pixel sequences in language space. Traditional approaches often rely on manually crafted pipelines, or on learning-based models with visual perception mechanisms, such as autoregressive pixel prediction models typically employing masked convolutions. In contrast, P$^2$-LLM aims to improve the understanding and prediction of pixels solely through a LLMing approach.

Key Methodologies

In response to the inadequacies of naive LLM-based compressors on high-dimensional data like RGB images, the paper introduces several pivotal innovations:

1. Integration of Pixel-Level Priors: The authors focus on leveraging intra-pixel inter-channel correlation and local self-similarity priors that are intrinsic to pixel sequences of images. This integration aims to enhance the ability of the LLM to understand complex and long-ranging dependencies inherent in RGB pixels.
2. In-context Learning via Pixel Prediction Chat Template: P$^2$-LLM introduces a specifically designed task prompt. This prompt seeks to contextualize the pixel prediction task in a language space, thereby increasing the LLM's ability to comprehend and predict pixel values more 'intelligently.'
3. Semantic Preservation Through Two-step Tokenization: The authors pride themselves on a two-step tokenization strategy that preserves pixel-level semantics. This allows the model to maintain a one-to-one mapping from pixel values to token representations, avoiding the information loss encountered in previous methods where proxy ASCII tokens led to degraded contextual understanding.
4. Predictive Distribution Sampling: The probability distribution for pixel predictions is sampled prior to softmax activation. This achieves a superior representation of probabilities tailored for arithmetic coding, benefiting the process by ensuring accurate encoding decisions are made.
5. Fine-tuning Through Low-rank Adaptation (LoRA): To overcome the limitations of generic pre-trained LLMs in domain-specific tasks like image compression, the authors incorporate fine-tuning strategies using LoRA. This process allows P$^2$-LLM to specialize in pixel prediction tasks to unlock its compression capabilities fully.

Results and Insights

The empirical evaluation demonstrates that P$^2$-LLM outperforms the previously leading codecs across a range of datasets, including natural images, screen content, and medical imagery. Notably, on challenging datasets such as CLIC.m and Kodak, P$^2$-LLM achieves impressive improvements, registering compression rates of 2.08 bpsp on the CLIC.m dataset and 2.83 bpsp on the Kodak dataset. These results underscore the potential of leveraging LLM architectures in image compression tasks traditionally driven by visually oriented models.

Implications and Future Directions

The success of P$^2$-LLM implies a potential shift in how researchers might approach data compression for visual content. Viewing image compression through the lens of a LLM expands the horizons for how multimodal data can be processed. This perspective highlights the flexible applicability of LLMs, indicating that the convergence of these areas could lead to innovative cross-domain solutions in the future.

Moreover, the results suggest future research directions, such as exploring end-to-end optimized tokenization strategies and improving LLM architectures to handle long-context dependencies more effectively. Furthermore, the paper opens a debate on the balance between model complexity and runtime efficiency, as the proposed solution, while effective, requires significant computational resources.

The sophisticated fine-tuning strategies and integration of rich semantic contexts may well be extended to other non-textual data modalities, potentially leading to breakthroughs in audio compression or even three-dimensional data compression for volumetric content.

In conclusion, "LLMs for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need" illustrates a significant step forward in data compression by adapting LLMs to a traditionally image-based domain. The implications of this work are profound, suggesting an exciting convergence of natural language processing capabilities with traditional data compression needs.