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The text-motion plate at CRF 36: clearly visible artefacts. Encoded from the original frames rather than transcoded from the reference, so it carries exactly one generation of loss and the comparison is clean. On this plate the damage shows as ringing around glyph edges, where legibility gives a pass/fail signal that needs no metric.
The text-motion plate at CRF 44: severe artefacts. Encoded from the original frames rather than transcoded from the reference, so it carries exactly one generation of loss and the comparison is clean. On this plate the damage shows as ringing around glyph edges, where legibility gives a pass/fail signal that needs no metric.
The text-motion plate at CRF 51 (extreme), the codec's limit artefacts. Encoded from the original frames rather than transcoded from the reference, so it carries exactly one generation of loss and the comparison is clean. On this plate the damage shows as ringing around glyph edges, where legibility gives a pass/fail signal that needs no metric.
The high-quality reference for the detail-chart compression ladder, at CRF 14. Each clip in this group is the SAME source frames encoded at a progressively higher quantiser, so the only variable is the encoder setting. Useful both for artefact-removal models and for calibrating a quality metric against settings whose visual cost is already known.
The high-quality reference for the gradient-sky compression ladder, at CRF 14. Each clip in this group is the SAME source frames encoded at a progressively higher quantiser, so the only variable is the encoder setting. Useful both for artefact-removal models and for calibrating a quality metric against settings whose visual cost is already known.
The high-quality reference for the text-motion compression ladder, at CRF 14. Each clip in this group is the SAME source frames encoded at a progressively higher quantiser, so the only variable is the encoder setting. Useful both for artefact-removal models and for calibrating a quality metric against settings whose visual cost is already known.
A conda explicit lock: an @EXPLICIT marker followed by one fully-resolved URL per fictional package with an md5 fragment, no solver involved, which is exactly what makes it a lockfile. Every package, version, hash and licence is fictional: the tree describes nothing real.
A form where a script adds and removes the mandatory flag at runtime. The attribute is absent from the shipped markup on purpose, so a validator that reads the file statically and one that reads the live DOM give different answers.
Colour ground-truth for the apple conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the cat conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the fruit conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the mug conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the plant conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Colour ground-truth for the robot conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Binary Canny edge map extracted from the ground truth. Feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
Tangent-space surface-normal map encoded from the scene depth: a normal-conditioning input for guided generation and relighting tests.
Sparse scribble derived from the edges: the loose hand-drawn control input for scribble-guided image generation.
Semantic segmentation map (subject vs background): the label reference for segmentation-conditioned generation and mask-quality scoring.
Binary Canny edge map extracted from the ground truth. Feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
Tangent-space surface-normal map encoded from the scene depth: a normal-conditioning input for guided generation and relighting tests.
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