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Record W2089848273 · doi:10.5555/2383795.2383812

Adaptive scalable texture compression

2012· article· en· W2089848273 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHigh Performance Graphics · 2012
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsTexelColor depthComputer scienceComputer visionLossy compressionArtificial intelligencePixelTexture compressionBilinear interpolationColor spaceColor imageImage textureImage segmentationSegmentationImage processingImage (mathematics)

Abstract

fetched live from OpenAlex

We describe a fixed-rate, lossy texture compression system that is designed to offer an unusual degree of flexibility and to support a very wide range of use cases, while providing better image quality than most formats in common use today. The system supports both 2D and 3D textures, at both standard and high dynamic range, at bit rates ranging from eight bits per pixel down to less than one bit per pixel in very fine steps. At any bit rate, texels can have from one to four color components. The system's flexibility results from a number of novel features. Color spaces and weights are represented using an encoding scheme that allows flexible allocation of bits between different types of information. The system uses bilinear interpolation to derive color space coordinates for a texel from sparse samples, and uses a procedural partition function to map texels to color spaces.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.250
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it