高分辨率文件的自适应迭代压缩：一种专注于在电影工作流中保留视觉质量的方法
Adaptive Iterative Compression for High-Resolution Files: an Approach Focused on Preserving Visual Quality in Cinematic Workflows
AI总结：
本研究提出一种用于高分辨率DPX衍生TIFF文件的迭代自适应压缩模型，通过SSIM和PSNR动态调整压缩参数，在保持高视觉保真度的同时实现高达83.4%的存储缩减，且优于JPEG2000和H.265。
摘要：
本研究提出了一种用于电影工作流和数字保存中高分辨率DPX衍生TIFF文件的迭代自适应压缩模型。该模型采用SSIM和PSNR指标，在三种配置（C0、C1、C2）下动态调整压缩参数，实现了高达83.4%的存储缩减，同时保持了高视觉保真度（SSIM > 0.95）。在三种不同制作——黑白经典片、柔和色调剧情片和复杂动作片——中的验证表明，该方法在保留关键视觉元素的同时显著降低了存储需求。专业评估人员报告称，最佳C1配置的接受率为90%，关键区域的伪影仍低于感知阈值。与JPEG2000和H.265的对比分析显示，在等效压缩率下具有更优的质量保留，特别是对于高位深内容。尽管需要额外的计算开销，该方法的存储优势和质量控制能力使其适用于专业工作流，并在医学成像和云存储优化中具有潜在应用。
英文摘要：
This study presents an iterative adaptive compression model for high-resolution DPX-derived TIFF files used in cinematographic workflows and digital preservation. The model employs SSIM and PSNR metrics to dynamically adjust compression parameters across three configurations (C0, C1, C2), achieving storage reductions up to 83.4 % while maintaining high visual fidelity (SSIM > 0.95). Validation across three diverse productions - black and white classic, soft-palette drama, and complex action film - demonstrated the method's effectiveness in preserving critical visual elements while significantly reducing storage requirements. Professional evaluators reported 90% acceptance rate for the optimal C1 configuration, with artifacts remaining below perceptual threshold in critical areas. Comparative analysis with JPEG2000 and H.265 showed superior quality preservation at equivalent compression rates, particularly for high bit-depth content. While requiring additional computational overhead, the method's storage benefits and quality control capabilities make it suitable for professional workflows, with potential applications in medical imaging and cloud storage optimization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
