Adaptive Iterative Compression for High-Resolution Files: an Approach Focused on Preserving Visual Quality in Cinematic Workflows
Leonardo Melo, Filipe Litaiff
TL;DR
The paper tackles the challenge of storing high‑resolution cinematographic content without compromising visual fidelity by introducing an adaptive iterative compression framework guided by SSIM and PSNR. It leverages TIFF as an efficient intermediate format and operates across three configurations (C0, C1, C2) to balance quality and compression. Validated on three real-world productions, the method achieves up to ~83% storage reduction with SSIM above 0.95 and a 90% professional acceptance rate for configuration C1, outperforming JPEG2000 and H.265 at similar bitrates—especially for high bit-depth content. While incurring extra computation, the approach offers practical benefits for professional workflows and digital preservation, with promising applicability to medical imaging and cloud storage optimization; future work includes broader format support and ML-assisted parameterization to further reduce overhead.
Abstract
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.