So... I'm assuming this is another timecube style website, right? The number of time subatomic, philosophy, dark matter and similar things are mentioned on a page about an image codec is weirdly high. AI generated paragraphs, no visual examples, no code, no real explanation how this works, ... and from what is there, I can't tell a difference between this and converting to a trained latent space.
Thrilled to share a white paper on the Relational Compression Format (RCF) codec, driven by the Unified Conceptual Framework / Grand Unified Tensor Theory (UCF/GUTT), tested on the Kodak dataset. RCF delivers compression ratios >85% (files <15% of PNG size), PSNR >30 dB, and SSIM >0.9, outperforming JPEG, PNG, WebP, and JPEG 2000 in efficiency and quality.
UCF/GUTT, introduced in The Relational Way: An Introduction (Amazon), the first book in a series, models reality as relational webs. RCF uses tensor-based math to encode image relationships, not pixels, achieving near-lossless results (e.g., SSIM 0.9430 on kodim07). It’s a novel approach for cloud storage, web delivery, AI vision, and eco-friendly tech.
Thrilled to share a white paper on the Relational Compression Format (RCF) codec, driven by the Unified Conceptual Framework / Grand Unified Tensor Theory (UCF/GUTT), tested on the Kodak dataset. RCF delivers compression ratios >85% (files <15% of PNG size), PSNR >30 dB, and SSIM >0.9, outperforming JPEG, PNG, WebP, and JPEG 2000 in efficiency and quality.
UCF/GUTT, introduced in The Relational Way: An Introduction (Amazon), the first book in a series, models reality as relational webs. RCF uses tensor-based math to encode image relationships, not pixels, achieving near-lossless results (e.g., SSIM 0.9430 on kodim07). It’s a novel approach for cloud storage, web delivery, AI vision, and eco-friendly tech.
The paper details Kodak results and UCF/GUTT’s relational roots: https://relationalexistence.com/comparison. How does RCF compare to deep learning codecs? Any thoughts on relational compression for sustainable tech? Feedback welcome!
So... I'm assuming this is another timecube style website, right? The number of time subatomic, philosophy, dark matter and similar things are mentioned on a page about an image codec is weirdly high. AI generated paragraphs, no visual examples, no code, no real explanation how this works, ... and from what is there, I can't tell a difference between this and converting to a trained latent space.
Thrilled to share a white paper on the Relational Compression Format (RCF) codec, driven by the Unified Conceptual Framework / Grand Unified Tensor Theory (UCF/GUTT), tested on the Kodak dataset. RCF delivers compression ratios >85% (files <15% of PNG size), PSNR >30 dB, and SSIM >0.9, outperforming JPEG, PNG, WebP, and JPEG 2000 in efficiency and quality. UCF/GUTT, introduced in The Relational Way: An Introduction (Amazon), the first book in a series, models reality as relational webs. RCF uses tensor-based math to encode image relationships, not pixels, achieving near-lossless results (e.g., SSIM 0.9430 on kodim07). It’s a novel approach for cloud storage, web delivery, AI vision, and eco-friendly tech. Thrilled to share a white paper on the Relational Compression Format (RCF) codec, driven by the Unified Conceptual Framework / Grand Unified Tensor Theory (UCF/GUTT), tested on the Kodak dataset. RCF delivers compression ratios >85% (files <15% of PNG size), PSNR >30 dB, and SSIM >0.9, outperforming JPEG, PNG, WebP, and JPEG 2000 in efficiency and quality.
UCF/GUTT, introduced in The Relational Way: An Introduction (Amazon), the first book in a series, models reality as relational webs. RCF uses tensor-based math to encode image relationships, not pixels, achieving near-lossless results (e.g., SSIM 0.9430 on kodim07). It’s a novel approach for cloud storage, web delivery, AI vision, and eco-friendly tech.
The paper details Kodak results and UCF/GUTT’s relational roots: https://relationalexistence.com/comparison. How does RCF compare to deep learning codecs? Any thoughts on relational compression for sustainable tech? Feedback welcome!