Similar Wavelet Conversion with minimal reprocessing : Smart Access : RS
(repeated encoding cost reduction) i know you are a coder, you could help ffmeg & avx on the FX8320E, Likewise consoles face same issue with FFPEG & Codecs & likewise with media acceleration by non repetition of encoding
Similar Wavelet Conversion with minimal reprocessing : Smart Access : RS
Printing Technology 'When you "Tie" the Knot' :
We want those Hand drawn Donald duck, Micky & Daffy in true line drawn splendour,
But hand drawing 8K is hell,
Remaster printing technology : For all monitors, TV's & Operating systems : DTS, Dolby : Functioning wave conversion
Smart-De-Compression : repeated encoding cost reduction : (c)Rupert S
Wavelet Classifiers
Audio
Video
Compressed Data, GZip, BZip, LZH
Primarily our goal is to Originate Encode in a form that is Compatable with the hardware chain,
For example in the case of HDD > CPU > GPU the right Texture & Number formats, Often 16Bit or 32Bit float & Texture,
However with Video we have to expand the frame wavelets into Compatable Texture formats!
We convert the Video Wavelet in Smart Access to the closest Texture format wavelet; Or directly play the video! But suppose we are using Bink Video? We directly convert & keep wavelets that are the same in the new texture,
We therefore select a texture format like NV12 or ETC2; One that has the most Similar Wavelets & can therefore reduce Conversion Cost of the frame by as much as 100% (If all wavelets are the same)!
We know Wavelet types & Colour depth of all texture classes; So we will select one with a good range,
In most cases we play MP4+ Wavelets; So we can Use a JPG type texture; So all the compression wavelets remain minimally processed.
A single Frame + previous B Frame; Into a single texture of the same Wavelet Compression Classification,
The result is minimal processing CPU Cycles.
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Overall reducing costs of higher resolution resolving; As available in 264 > 265 > 266/VVC & other Media Encoders : Rupert S
You can see that, formats such as 265 & 264 are related, Obviously at a higher resolution in the case of 265!
But in many Wavelet transform cases we can minimise the Processing cost, We do however need to know like Google's ML Voice Encoder; The ones we do not need to change (minimum benefaction)
My chief challenge of Wavelet thought is a multiple frame picture of an eye (WebP for example),
The resolution is 640x480 & We know in most probabilities that; The Eye was transformed to wavelet in HD,
So we have a wavelet curve; Black centre & A surrounding Iris!
We need to expand that wavelet so we will suppose that the higher precision version of the wavelet will add details?
We must explore how the wavelet transforms a Higher Resolution form into a lower resolution form,
We can therefore in theory use the same wavelet at higher resolving depth?
We might be able to convert a lower resolving wavelet in 12Bit into the 16Bit version & have a better understanding of the higher quality version!
We can therefore most probably reuse the wavelet; Transforming from 264 to 265 & upscale & compress more,
Overall reducing costs of higher resolution resolving; As available in 264 > 265 > 266/VVC
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#WaveletProve Both that the wavelet is infinite & that; The Breton
shirt wavelet has a pattern represented in 12Bit but liberating into
the profound on 16Bit, 32Bit & more!
(To understand wavelet context, in textile & theory & of course Audio & Video)
Can we prove the wavelet of a Breton shirt for infinity, like mauri
My augment being that we can upscale that Breton shirt! & prove it's
17th century values...
Both that the wavelet is infinite & that; The Breton shirt wavelet has
a pattern represented in 12Bit but liberating into the profound on
16Bit, 32Bit & more!
Example Wavelets to prove upscaling is possible
https://is.gd/WaveletData*
Rupert S
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Wavelet Upscaling : JPG / Video / Games
Example 2 Voxel to High Quality : RS
The Story : HP : V-FX Wavelet Voxel Transforms : V-FX-WVT (c)RS (Harry Potter + More)
I was wondering what to add to Wavelet transforms; Well i was thinking about Harry Potter,
Full body FX are Half Resolution; In Fact they are Depth of Field Voxels,
For people who don't know Voxel is when you make a Cube of the right shade from a picture & set it at the right depth!
For those criticizing such an act as lazy; You would have to understand how fast technology has developed!
Some characters Fly at a very low resolution & Others like Harry Potter & Melfoy Don't!
You would have to realise that V-FX is based on the ability of the person to be in the role... They perform ;-)
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V-FX Wavelet Voxel Transforms : V-FX-WVT (c)RS (Harry Potter + More)
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Definitions
The Wavelet is the JPG Pixel Group of a single Group of pixels at the same size as the composing Voxels of the V-FX
A Voxel is a Cube of Pixels set in 3D
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When it comes to Transforms; This piece is called:
Transforms for classic movies : How you upscale VFX : RS
Firstly the VOXEL (Simple Wavelet Cube) needs to be compared to a fully dressed original character,
Then you need to map the correct features into The voxel cube space; After you Average Anti-Alias & Upscale the Cube Map (Original V-FX + Original Video Frame Person)
You then need to map an effective Wavelet of the Original V-FX with a modifier Layer of transparent Wavelet (The Photo in High Detail, This is also a Wavelet Series)
(c)RS
*Example 3 : Lessons to learn : Wavelets : Upscaling (c)RS
Now about the Voxel 4x4 cube map 'Transform wavelet' is a simple JPG Wavelet
(if used properly compressed & older games did not because processors where not very fast (33Mhz)
High resolution 'Transform Wavelet' (Overlayed) is a full to higher resolution JPG Wavelet
In Upscaling we need to get from one to the other,
Transform Wavelet from Voxel Wavelet,
Sample Scaling:But supposing we have samples of like minded objects?
We can use Machine Learning to imprint a pattern!
But great looking as this is, not perfect as seen in Example 3 About Example 2 : HP!
Wavelet permutation:
Resolve the wavelet to full precision, Workable; But we need to know the result is correct!ML Can help; But that is very subjective..
Mostly this works.
Identity Follow through:
Machine Learning that identifies the subject matter [Samsung & LG TV's 2020+ Example]
So what do we do? We Add the lot! haha
Rupert S
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Example 4 : Lessons to learn : Wavelets : Upscaling (c)RS
2 Pattern Matrix Wavelet (c)RS
Wavelets are patterns; With Colour infilling (why not a wavelet itself!
Well wavelets come in forms (Gif)8Bit, 10Bit, 12Bit, 16Bit(JPG)
We can advance the precision by using a higher Precision (16Bit, 24Bit, 32Bit); But we need to save storage space!
First thing is to use bF16 & bF32; This keeps the majority of the data from being sub pixels.
Second thing is to make maximum use of multiple Precisions, Mix F16 with F32..
Google Lyra Codec demonstrates this in Machine Learning.
Third : Keep Precision within margins, Small Textures do well in 8Bit Matrix Wavelets...
But 16Bit Colour Precision & 16Bit Precision both look good in HD High Quality HDR WCG
(Usable as encryption archetype): Chaos:A:B:T:Pi:Arc:Sin:Tan
Very usable /dev/rnd Random Ring : TRNG : GPU : CPU : Asics : Using Chaos Wavelet
{Wavelet:Colour Point) A to B as expression of Pi
{Wavelet:Colour Point} A to B as expression of Arc, Sin, Tan
[2PMW File Array]
[Header : Easy Identifier : Basic Name]
{Header Packed Wavelet Groups] [1 Image Wavelet : Colour Shading Wavelet 2, 4, 8 Group]
[Image Array lines]
|Packed Groups of] : [ Image Wavelet 1 : Colour Shading Wavelet Associations, 1 to 8]
[Packed Groups of] : [ Image Wavelet 1 : Colour Shading Wavelet Associations, 1 to 8]
[Packed Groups of] : [ Image Wavelet 1 : Colour Shading Wavelet Associations, 1 to 8]
[PG],[PG],[PG],[PG],[PG]
[PG],[PG],[PG],[PG],[PG]
[PG],[PG],[PG],[PG],[PG]
[PG],[PG],[PG],[PG],[PG]
[PG],[PG],[PG],[PG],[PG]
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Audio/Video/Image Format : Packing Vectors (c)RS
Vector Wavelet Examples : Math object
Wavelet Curve compress, Normally from left because we code Left to right & that is optimal for our hardware.
Can be numeric sequence Direction point 1=D D=1,2,3,4 2=Db = 1,2,3,4 | Displacement Dp = 1,2,3,4 Assuming Left To Right or curve displacement = Time
Distance N from source edge, Curve:Sin/Tan
(Example) D=1 Db=3 Dp1=2 Dp2=3 | Curve = Tan3+Db2
Logarithmic Pack,
Integer Comparator : N+N2+N3=N+1+2+3 | Sequence
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Example 5 : Predict Scaling : SiMD/AVX.SSE3 : (c)RS
SiMD Interpolation grids & Predict with Raytracing & General SiMD
Reference Grid
https://science.n-helix.com/2023/03/path-trace.html
https://science.n-helix.com/2022/08/jit-dongle.html
With the Interception/Processing of Predict Statements in Frames of Video & Audio; Using a simple Grid:
Pr = Predict (motion) Px = Pixel t1:2:3 time period
PxPx1PxPxPx3
Pr1Pr2PxPx2Px
Px1PxPr3PxPx
Px1Pr2PxPxPx
Px1PxPr2PxPx
Basically you can see the pixels move in frame Px1 & Predicted in Pr2 & Pr3,
Raytracing SiMD predict future motion though maths; We can use the SiMD to,
Both predict & interpolate/Upscale from 8bit, 10Bit, 12Bit, 14Bit to 16Bit values or rather wavelets,
Because Raytracing SiMD are high precision maths; They prove advantageous if we have them; SiMD/AVX.SSE3
Interpolation : Prxi Pxri : {PxPrPi} Theory : RS
We must present a point between Px (pixel) & Pr (predict); In maths this would be a remainder,
We can draw a pixel in the Remainder Point; The Interpolation point (PI); When? When we upscale!,
We can use two principles, Px (actual pixel), Pr (Predicted Pixel), PI Pixel Interpolation!
We can guess with both Px & Pr on the content of PI & both Predict & Interpolate the pixel...
As additional Data; This does not worry us a lot.
PxPIPxPxPI
PIPxPrPIPx
PrPrPxPiPr
(c)Rupert S
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Interpolation & Extrapolation Policy : RS
We can conclude Interpolation & Tessellation have requirements : 2D & 3D Spline Interpolation & Extrapolation; Gaussian methods on linear surfaces,
We extrapolate the new; Such as blade edge; We can however layout a simple grid to our supposition edge & interpolate.
We do not need to extrapolate where we have planed to draw; With so much as a 3cm polygon with 4 Lines & 2 edges,
We can however draw a fractal blade; For example : HellSinger from Elric Melbone.
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https://sg.indeed.com/career-advice/career-development/interpolation-vs-extrapolation
Massive Datasets https://www.aimsciences.org/DCDS/article/2023/43/3&4
Python Libraries Interpolation:
15 Types
https://help.scilab.org/section_64fa3f01fdb19353faf0c6806a64a533.html
Gaussian
https://gmd.copernicus.org/articles/16/1697/2023/
https://gmd.copernicus.org/articles/16/1697/2023/gmd-16-1697-2023.pdf
JIT Compile Displacement Micromap : Interpolation & Extrapolation Policy : RS
Compress its internal geometry representations into the compressed format Just in time,
Optimizing, Allocating & de-allocating in accord with Mesh Shaders & Cache availability.
VK_NV_displacement_micromap, which for Vulkan ray-tracing can help with added detail
No Comment https://www.phoronix.com/news/Vulkan-1.3.245-Released
VK_NV_displacement_micromap allows a displacement micromap structure to be attached to the geometry of the acceleration structure,
allow the application to compress its internal geometry representations into the compressed format ahead of time.
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Our options for interpolation (don't forget Gaussian)
bsplin3val — 3d spline arbitrary derivative evaluation function
cshep2d — bidimensional cubic shepard (scattered) interpolation
eval_cshep2d — bidimensional cubic shepard interpolation evaluation
interp — cubic spline evaluation function
interp1 — 1D interpolation in nearest, linear or spline mode
interp2d — bicubic spline (2d) evaluation function
interp3d — 3d spline evaluation function
interpln — linear interpolation
linear_interpn — n dimensional linear interpolation
lsq_splin — weighted least squares cubic spline fitting
mesh2d — Triangulation of n points in the plane
smooth — smoothing by spline functions
splin — cubic spline interpolation
splin2d — bicubic spline gridded 2d interpolation
splin3d — spline gridded 3d interpolation
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2D-3D Spline Interpolations with background complementary colour layer smooth blend
Right on the kindle paper white 2D Spline is good for a single layer, 3D Spline is good if you rasterize a shader behind the text and shade it: The method would not cost over 1% of processing power on a 2 core ARM 400Mhz, If the image is relatively static.
On full Colour HDR WebBrowser, The 3D Spline method makes sense with complementary colour blending...
On mostly static content; 3% of total page processing costs.
On mostly Static Text with mobile images a combination of 2D & 3D Spline; 7% to 15% of cost.
interp2d — bicubic spline (2d) evaluation function
interp3d — 3d spline evaluation function
Rupert S
Specification for Open Compute & Gaussian Interpolation & JIT Compile
Displacement Micromap : Interpolation & Extrapolation Policy : RS
https://science.n-helix.com/2023/02/smart-compression.html
https://drive.google.com/file/d/1C3Q9-LvB0T8p6XHpoZynttxuV2Eunwg2/view?usp=sharing,
https://drive.google.com/file/d/1KxxKRLOH01m5IYqAy9DeR9qq8gHIEdSs/view?usp=sharing,
https://drive.google.com/file/d/1SYLr0JwWD-DbbXHsrANxkFe2hBrn1cZf/view?usp=sharing,
https://drive.google.com/file/d/1c2K5GooOKY-kPHxiqc27A_l3pkcYxvZU/view?usp=sharing,
https://drive.google.com/file/d/1sjMpGVhvULsSloeoQ_zikzX2AzZlUBtY/view?usp=sharing
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https://is.gd/WaveletDataTexture Compressors
https://github.com/BinomialLLC/basis_universalhttps://github.com/darksylinc/betsyTo Compress using CPU/GPU: MS-OpenCL
https://is.gd/MS_OpenCLhttps://is.gd/OpenCL4X64https://is.gd/OpenCL4ARMPoCL Source & Code
https://is.gd/LEDSource
Khronos-1.3Extens*
The Smart-access
[Innate Compression, Decompression, QoS To Optimise the routing, Task Management To optimise the process] : Task Managed Transfer : DMA:PIO : Transparent Task Sharing Protocols
The following is the initiation of the Smart-access Age
QoS To Optimise the routing:Task Management To optimise the process
https://science.n-helix.com/2021/11/monticarlo-workload-selector.html
https://science.n-helix.com/2023/02/pm-qos.html
https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html
https://science.n-helix.com/2023/03/path-trace.html
Transparent Task Sharing Protocols
https://science.n-helix.com/2022/08/jit-dongle.html
https://science.n-helix.com/2022/06/jit-compiler.html
Innate Compression, Decompression
https://science.n-helix.com/2022/03/ice-ssrtp.html
https://science.n-helix.com/2022/09/ovccans.html
https://science.n-helix.com/2022/08/simd.html
Examples of compression
https://godotengine.org/article/betsy-gpu-texture-compressor/https://github.com/darksylinc/betsy/blob/master/Docs/technical_doc_advanced.md
