Adaptive Iterative Compression for High-Resolution Files: an Approach Focused on Preserving Visual Quality in Cinematic Workflows

TL;DR

This paper introduces an adaptive iterative compression method for high-resolution cinematographic files that significantly reduces storage needs while preserving visual quality, validated across diverse productions and outperforming existing codecs.

Contribution

The study proposes a novel adaptive compression model using SSIM and PSNR metrics, achieving high compression ratios with minimal perceptual artifacts in professional cinematic workflows.

Findings

Up to 83.4% storage reduction with SSIM > 0.95

90% professional acceptance for optimal configuration

Outperforms JPEG2000 and H.265 in quality preservation at similar compression levels

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.