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The full-resolution reference for the detail-chart super-resolution set at 640x360. Every low-resolution input in this group was produced by downscaling these exact pixels with a recorded filter, so an upscaler's output can be compared against the true original instead of against another upscale.
The full-resolution reference for the pan-city super-resolution set at 640x360. Every low-resolution input in this group was produced by downscaling these exact pixels with a recorded filter, so an upscaler's output can be compared against the true original instead of against another upscale.
The full-resolution reference for the text-motion super-resolution set at 640x360. Every low-resolution input in this group was produced by downscaling these exact pixels with a recorded filter, so an upscaler's output can be compared against the true original instead of against another upscale.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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