Adaptive Iterative Compression for High-Resolution Files: an Approach Focused on Preserving Visual Quality in Cinematic Workflows
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910801798889472 |
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| author | Melo, Leonardo Litaiff, Filipe |
| author_facet | Melo, Leonardo Litaiff, Filipe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Iterative Compression for High-Resolution Files: an Approach Focused on Preserving Visual Quality in Cinematic Workflows Melo, Leonardo Litaiff, Filipe Computer Vision and Pattern Recognition Emerging Technologies Machine Learning Performance 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. |
| title | Adaptive Iterative Compression for High-Resolution Files: an Approach Focused on Preserving Visual Quality in Cinematic Workflows |
| topic | Computer Vision and Pattern Recognition Emerging Technologies Machine Learning Performance |
| url | https://arxiv.org/abs/2501.16319 |