Deblur Input — detail-chart, Horizontal motion blur, 9 px
Horizontal motion blur, 9 px applied to the sharp detail-chart plate with a uniform horizontal box kernel — a linear camera pan during exposure. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
Browser-playable test video · 2 seconds
Specifications
- Resolution
- 640x360
- Fps
- 24
- Blur Type
- motion
- Kernel Size
- 9
- Role
- deblur input
- Kernel
- uniform horizontal box
- Reference
- vid-deblur-detail-chart-gt
- Crf
- 19
- Base Plate
- detail-chart
Testing contract
Expected to pass- Scenario
- Deblur the clip and score the result against the sharp ground truth.
- Expected result
- The blur is motion with a 9-tap uniform horizontal box kernel, applied to the detail-chart plate at 640x360 24 fps; the reference is vid-deblur-detail-chart-gt. The kernel is known exactly, so this is a non-blind deconvolution fixture: a model given the kernel and a model that estimates it can be compared on identical input.
What is a .mp4 file?
MP4 (MPEG-4 Part 14) is the dominant container for digital video, holding video, audio, subtitle, and metadata tracks in a tree of typed boxes. `ftyp` declares the brand, `moov` carries the sample tables that make seeking possible, and `mdat` holds the media; when an encoder writes `moov` last, playback cannot begin until the file has fully downloaded, which a faststart remux fixes. It derives from Apple's QuickTime format, generalized by ISO into the ISOBMFF base that MOV, 3GP, and HEIF share.
How to use this file
Use an example MP4 to test box parsing and track detection, seeking, range-request streaming, and transcode or thumbnail pipelines: verifying that a file with a trailing `moov` box is still handled, and that codec support is checked per track rather than inferred from the extension.
How to use this file for testing
“Deblur Input — detail-chart, Horizontal motion blur, 9 px” is a deterministic Novus Examples fixture for Video deblur, Video QA. Motion-blurred and defocused clips produced from a sharp source with a recorded kernel, so deblurring output can be measured against the original rather than compared to another blurred frame.
Documented properties for this file: deblur input · 24 fps. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Media fixtures are short and synthetic by design. Prefer waveform or transcript ground truth in the same group when measuring ASR, trim, upscale, or sync tools; do not assume broadcast-quality masters.
Code examples
<video controls preload="metadata" width="640" src="detail-chart-motion-h.mp4"></video>Related files
- mp4Deblur Ground Truth — text-motion, SharpThe sharp reference for the text-motion deblur set. Each blurred clip in this group applies a documented kernel to these exact pixels, so the deconvolution problem has a known answer — including which detail was destroyed outright and therefore cannot be recovered, only invented.
- mp4Deblur Input — text-motion, Defocus blur, radius 5Defocus blur, radius 5 applied to the sharp text-motion plate with a Gaussian kernel — a defocused lens. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
- mp4Deblur Input — text-motion, Horizontal motion blur, 21 pxHorizontal motion blur, 21 px applied to the sharp text-motion plate with a uniform horizontal box kernel — a linear camera pan during exposure. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
- mp4Deblur Input — text-motion, Horizontal motion blur, 9 pxHorizontal motion blur, 9 px applied to the sharp text-motion plate with a uniform horizontal box kernel — a linear camera pan during exposure. The kernel is documented, so this supports both blind and non-blind deconvolution: give the algorithm the kernel, or make it estimate one and compare.
- mp4Interpolation Input — depth-layers, Motion-Blurred DecimationHalved frame rate where each output frame is the AVERAGE of the two it replaces, rather than one of them — what a long shutter angle actually produces. Substantially harder than clean decimation, because the interpolator must undo motion blur as well as invent the missing instants, and no input frame matches any ground-truth frame exactly.
- mp4Interpolation Input — orbit-solid, Motion-Blurred DecimationHalved frame rate where each output frame is the AVERAGE of the two it replaces, rather than one of them — what a long shutter angle actually produces. Substantially harder than clean decimation, because the interpolator must undo motion blur as well as invent the missing instants, and no input frame matches any ground-truth frame exactly.
Generated by generation/video_ai_suites.py. Free for any use, no attribution required, license.