While the HPEZ framework presents significant advancements in error-bounded lossy compression, several potential limitations and drawbacks warrant consideration:
Computational Complexity: The advanced interpolation techniques and auto-tuning processes may introduce additional computational overhead, particularly for large datasets. Future work could focus on optimizing these algorithms to reduce their computational complexity, possibly through parallel processing or hardware acceleration (e.g., GPU utilization).
Adaptability to Diverse Data Types: Although HPEZ is designed for structured data grids, its performance may vary with unstructured or semi-structured data types. Future research could explore the adaptability of HPEZ's techniques to a broader range of data formats, including text and non-grid-based data, by developing specialized interpolation methods.
Quality Metrics Limitations: The current framework primarily focuses on traditional quality metrics such as PSNR and SSIM. However, these metrics may not fully capture perceptual quality, especially in images and videos. Future enhancements could involve integrating perceptual quality metrics that better align with human visual perception, thereby improving the subjective quality of the compressed outputs.
Error Propagation: In lossy compression, errors can propagate through subsequent processing stages, potentially degrading the quality of the final output. Future work could investigate methods to mitigate error propagation, such as incorporating error correction techniques or adaptive error management strategies.