Testaroo, Real, documented example files for testing

Super-Resolution Input: detail-chart, ÷2 Area / box

The detail-chart plate downscaled 2× to 320x180 using a area / box filter. Simple pixel averaging, what a camera's binning path actually does. Softer than bicubic and not what most models saw in training, which makes it a fairer test. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: detail-chart, ÷2 Bicubic

The detail-chart plate downscaled 2× to 320x180 using a bicubic filter. The standard downscale in most benchmarks. Mild ringing at edges, and the filter most super-resolution models are trained to invert, so it flatters them. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: detail-chart, ÷2 Nearest neighbour

The detail-chart plate downscaled 2× to 320x180 using a nearest neighbour filter. Point sampling with no filtering at all, so downscaling aliases hard. The Siemens star folds into moiré, and no amount of upscaling can recover what aliasing destroyed. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: detail-chart, ÷3 Area / box

The detail-chart plate downscaled 3× to 212x120 using a area / box filter. Simple pixel averaging, what a camera's binning path actually does. Softer than bicubic and not what most models saw in training, which makes it a fairer test. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: detail-chart, ÷3 Bicubic

The detail-chart plate downscaled 3× to 212x120 using a bicubic filter. The standard downscale in most benchmarks. Mild ringing at edges, and the filter most super-resolution models are trained to invert, so it flatters them. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: detail-chart, ÷3 Nearest neighbour

The detail-chart plate downscaled 3× to 212x120 using a nearest neighbour filter. Point sampling with no filtering at all, so downscaling aliases hard. The Siemens star folds into moiré, and no amount of upscaling can recover what aliasing destroyed. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: detail-chart, ÷4 Area / box

The detail-chart plate downscaled 4× to 160x90 using a area / box filter. Simple pixel averaging, what a camera's binning path actually does. Softer than bicubic and not what most models saw in training, which makes it a fairer test. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 160x90
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: detail-chart, ÷4 Bicubic

The detail-chart plate downscaled 4× to 160x90 using a bicubic filter. The standard downscale in most benchmarks. Mild ringing at edges, and the filter most super-resolution models are trained to invert, so it flatters them. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 160x90
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: detail-chart, ÷4 Nearest neighbour

The detail-chart plate downscaled 4× to 160x90 using a nearest neighbour filter. Point sampling with no filtering at all, so downscaling aliases hard. The Siemens star folds into moiré, and no amount of upscaling can recover what aliasing destroyed. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 160x90
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷2 Area / box

The pan-city plate downscaled 2× to 320x180 using a area / box filter. Simple pixel averaging, what a camera's binning path actually does. Softer than bicubic and not what most models saw in training, which makes it a fairer test. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷2 Bicubic

The pan-city plate downscaled 2× to 320x180 using a bicubic filter. The standard downscale in most benchmarks. Mild ringing at edges, and the filter most super-resolution models are trained to invert, so it flatters them. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷2 Nearest neighbour

The pan-city plate downscaled 2× to 320x180 using a nearest neighbour filter. Point sampling with no filtering at all, so downscaling aliases hard. The Siemens star folds into moiré, and no amount of upscaling can recover what aliasing destroyed. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷3 Area / box

The pan-city plate downscaled 3× to 212x120 using a area / box filter. Simple pixel averaging, what a camera's binning path actually does. Softer than bicubic and not what most models saw in training, which makes it a fairer test. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷3 Bicubic

The pan-city plate downscaled 3× to 212x120 using a bicubic filter. The standard downscale in most benchmarks. Mild ringing at edges, and the filter most super-resolution models are trained to invert, so it flatters them. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷3 Nearest neighbour

The pan-city plate downscaled 3× to 212x120 using a nearest neighbour filter. Point sampling with no filtering at all, so downscaling aliases hard. The Siemens star folds into moiré, and no amount of upscaling can recover what aliasing destroyed. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷4 Area / box

The pan-city plate downscaled 4× to 160x90 using a area / box filter. Simple pixel averaging, what a camera's binning path actually does. Softer than bicubic and not what most models saw in training, which makes it a fairer test. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 160x90
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷4 Bicubic

The pan-city plate downscaled 4× to 160x90 using a bicubic filter. The standard downscale in most benchmarks. Mild ringing at edges, and the filter most super-resolution models are trained to invert, so it flatters them. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 160x90
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: pan-city, ÷4 Nearest neighbour

The pan-city plate downscaled 4× to 160x90 using a nearest neighbour filter. Point sampling with no filtering at all, so downscaling aliases hard. The Siemens star folds into moiré, and no amount of upscaling can recover what aliasing destroyed. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 160x90
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: text-motion, ÷2 Area / box

The text-motion plate downscaled 2× to 320x180 using a area / box filter. Simple pixel averaging, what a camera's binning path actually does. Softer than bicubic and not what most models saw in training, which makes it a fairer test. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: text-motion, ÷2 Bicubic

The text-motion plate downscaled 2× to 320x180 using a bicubic filter. The standard downscale in most benchmarks. Mild ringing at edges, and the filter most super-resolution models are trained to invert, so it flatters them. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: text-motion, ÷2 Nearest neighbour

The text-motion plate downscaled 2× to 320x180 using a nearest neighbour filter. Point sampling with no filtering at all, so downscaling aliases hard. The Siemens star folds into moiré, and no amount of upscaling can recover what aliasing destroyed. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 320x180
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: text-motion, ÷3 Area / box

The text-motion plate downscaled 3× to 212x120 using a area / box filter. Simple pixel averaging, what a camera's binning path actually does. Softer than bicubic and not what most models saw in training, which makes it a fairer test. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: text-motion, ÷3 Bicubic

The text-motion plate downscaled 3× to 212x120 using a bicubic filter. The standard downscale in most benchmarks. Mild ringing at edges, and the filter most super-resolution models are trained to invert, so it flatters them. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

Super-Resolution Input: text-motion, ÷3 Nearest neighbour

The text-motion plate downscaled 3× to 212x120 using a nearest neighbour filter. Point sampling with no filtering at all, so downscaling aliases hard. The Siemens star folds into moiré, and no amount of upscaling can recover what aliasing destroyed. Upscale it back to 640x360 and score against the ground truth in this group: the filter is recorded because which one was used changes the difficulty far more than the scale factor does.

File: MP4 · Superres · 212x120
Use case: Video upscalingVideo QA· Conversion set

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