## Introduction The amount of scientific data is growing exponentially, making it essential to develop efficient compression techniques. However, evaluating the quality of data after compression can be a computationally intensive calculation. In this context, a team of researchers has presented DeepCQ, an innovative platform for predicting compression quality. This solution was designed to be generalizable across various applications and compression technologies. ## Key Features DeepCQ presents three key features: * A surrogate modeling approach to compress quality that can be adapted to different needs; * A two-phase project that separates the computational data extraction phase from the lightweight prediction of metrics phase; * An optimization of performance on evolving information using a mixed design expertise. This approach enables DeepCQ to achieve exceptional precision in predicting compression quality, with errors generally below 10%. ## Validation Results show that DeepCQ significantly surpasses existing solutions, making it an excellent choice for scientific users who want to make informed decisions about data compression. ## Conclusion In summary, DeepCQ represents a significant turning point in managing scientific data. Its ability to predict compression quality with precision can greatly reduce the I/O and computational load required to analyze data. ## Future Prospects The research team is now focusing on validating the performance of DeepCQ across various real-world applications, aiming to further improve the accuracy of the compression quality prediction.