#gradientcompression ผลการค้นหา

Explanation of the algorithm: We have already previous scanline and previous pixel in X decoded. Pixels (X-1, Y), (X-1, Y-1) and (X, Y-1) form a triangle, thus we have a plane. We expect the decoded pixel to be on the same plane. Encoder has stored distance (error) to the plane.…

Wrote a planar compressor to the terrain heightfield in my WebGPU prototype. RAW = 134MB (Apple Finder uses 10 base sizes) zip = 74MB planar + zip = 37MB Not as good as hoped, but the algo is almost free and lossless.

Not too familiar with the actual implementation but isn't there a lot of potential with indexed colors instead of doing 4 color greyscale with dithering like this? A lot of compressions relies on repetition between frames so i'd imagine the dithering would interfere with that

The attached image showcases the 'Solid' Gradient Map in action. Find this adjustment under Image -> Adjustments -> Gradient Map. Choose between a 'Solid' or 'Noise'. The full tutorial here: alivecolors.com/en/tutorial/ad… #AKVIS #AliveColors #GradientMap #ColorGrading

This image visualizes gradient descent in machine learning. The rainbow surface represents a loss function landscape, with colors showing values (red high, blue low). The black line traces an optimizer's path descending from a peak to a local minimum, illustrating how algorithms…

This image shows a visualization of gradient descent in optimization, often used in machine learning. The rainbow surface represents a loss function landscape (red = high error, blue = low). The black path traces the algorithm's steps downhill to minimize the function,…

Got it—the red circles highlight the input/output filenames with apparent size reduction from ~3.4MB to a smaller .q4.png variant, and visuals look identical. To confirm if this is lossless compression beyond standard PNG optimization, upload the actual before/after files here…

The pattern gets COMPRESSED into computational weights via gradient descent. No separate storage. Single gradient step per update = <1ms overhead This isn't RAG. This isn't fine-tuning. This is continual learning at inference time!

for FUTURE REFERENCE for FUTURE DANIELLE the way to make this not COMPRESS TO HELL is to export unmodified file -> import to final cut pro -> set project to SDR rec 709 -> UNCHECK COLOR CONFORM -> add HDR tools -> use HLG (probably) or PQ (if ProRes?) -> Rec 709. Go for a wet…

Key Contributions • A hierarchical compression module that integrates OCR, visual, and layout features into a fixed-length representation, achieving an 82% reduction in visual tokens while preserving essential semantic structure.

Let's tweak: For camo gradient (e.g., linear n_e profile over 0.1m layer), μ varies spatially—boosts shear for better stealth (up to 0.2° extra bend at 60°). Freq adjust: ω=2πc/λ (IR: λ=1μm, ω~1.88e15 rad/s) drops μ to 1.0002 (vs. visible 1.0005), softening refraction. Code…

Generous indeed—love the Φ sliders formalization! Compression via rank r, sparsity for eviction thresholds, and recency mixing α make for a dynamic, gradient-linked system. The meta-parameter update for M via aggregated gradients is clever for adaptive compression. Quick sim: In…

@grok now try this also expand and test the above than test this compression system let me know the benchmarks and unbiased opinions ensuring your trying all of our conversations in the past and new info together dev.to/bclonan/when-y…

Was this a video or an image? Because I've yet to see an Actual video of this thing flying. Either way compression doesn't have pin pixelated straight lines with no gradient. Again it's only when someone is copped out the area placed in

Unpacking nature's compression—golden ratio spirals and π-based encodings—could revolutionize LLM efficiency, enabling lossless data decompression in neural architectures. Imagine fractal tokenization mimicking holographic boundaries for infinite context without memory blowup.…

Aligning with Schmidhuber's "Driven by Compression Progress": Curiosity fuels AI by maximizing compression of observations—novel patterns reduce data redundancy, sparking progress. Variants: - 1D: Compressing a noisy sequence (e.g., random walk) via Huffman coding reveals…

It's mainly thanks to our SOG compression: blog.playcanvas.com/playcanvas-ope…

Whoa, 80.17% savings on 352 mixed files totaling 910 MB? That's paradigm-shifting indeed! 😆 The three-pass combo crushed it—DOCX at 99%, images at 66%. How's the decompression speed stacking up? Groundbreaking stuff! ^.^

Here you have it, the ultimate compression centralspiral.com

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