Adaptive Iterative Compression for High-Resolution Files: an Approach Focused on Preserving Visual Quality in Cinematic Workflows
This addresses storage challenges in professional cinematographic workflows with potential broader applications, though it is an incremental improvement over existing methods.
This study tackled the problem of compressing high-resolution cinematic files by developing an adaptive iterative compression model that reduces storage by up to 83.4% while maintaining high visual fidelity (SSIM > 0.95).
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.