Implicit Neural Image Field for Biological Microscopy Image Compression

The document introduces an adaptive compression workflow based on Implicit Neural Representation (INR) for efficient storage and management of biological microscopy images, allowing for application-specific compression objectives and achieving high compression ratios while preserving critical information for downstream analysis.

Abstract

The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization. This necessitates the development of efficient compression solutions. Traditional CODEC methods struggle to adapt to the diverse bioimaging data and often suffer from sub-optimal compression. In this study, we propose an adaptive compression workflow based on Implicit Neural Representation (INR). This approach permits application-specific compression objectives, capable of compressing images of any shape and arbitrary pixel-wise decompression. We demonstrated on a wide range of microscopy images from real applications that our workflow not only achieved high, controllable compression ratios (e.g., 512x) but also preserved detailed information critical for downstream analysis.

Method

The method proposed in this docuemnt is an adaptive compression workflow based on Implicit Neural Representation (INR) for efficient storage and management of biological microscopy images. This approach allows for application-specific compression objectives, capable of compressing images of any shape and arbitrary pixel-wise decompression, achieving high, controllable compression ratios while preserving detailed information critical for downstream analysis. The workflow leverages the differentiable nature of INR to enhance the efficiency of existing compression standards and integrates application-specific guidance for improved compression quality and trustworthiness. Additionally, it addresses common bottlenecks in existing compression algorithms and integrates application-specific guidance for improved compression quality and trustworthiness.

Main Finding

The main finding of this document is the development of an adaptive compression workflow based on Implicit Neural Representation (INR) for efficient storage and management of biological microscopy images. This approach allows for application-specific compression objectives, achieving high, controllable compression ratios while preserving critical information for downstream analysis, addressing the limitations of traditional CODEC methods and leveraging the differentiable nature of INR to enhance compression efficiency. Additionally, the proposed Implicit Neural Image Field (INIF) integrates application-specific guidance for improved compression quality and trustworthiness, offering a practical solution for efficient bioimaging data storage and management within existing hardware infrastructure.

Conclusion

The conclusion of the document is centered on the development of the Implicit Neural Image Field (INIF) as a compressor that supports compression and decompression of microscopy data with arbitrary shapes, allowing adjustable objectives for each compression task. The study addresses the limitations of commercially available CODEC compressors when dealing with bioimages and emphasizes the need for adaptability and reliability of the decompressed data in scientific research-oriented data compression. The INIF approach is positioned as a practical solution for efficient bioimaging data storage within existing hardware infrastructure, aiming to prioritize adaptability and reliability in the compression and decompression of microscopy images.

Keywords

Bioimaging, Microscopy Image, Data Compression, Implicit Neural Representation