Deblur Input — detail-chart, Defocus blur, radius 5
Defocus blur, radius 5 applied to the sharp detail-chart 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.
Specifications
- Resolution
- 640x360
- Fps
- 24
- Blur Type
- defocus
- Kernel Size
- 5
- Role
- deblur input
- Kernel
- Gaussian
- Reference
- vid-deblur-detail-chart-gt
- Crf
- 19
- Base Plate
- detail-chart
What is a .mp4 file?
MP4 (MPEG-4 Part 14) is a widely supported multimedia container based on ISOBMFF that holds video, audio, subtitles, and metadata, most commonly H.264 or H.265 video with AAC audio. It supports streaming, chapters, and multiple tracks. It is the dominant format for distributing and playing digital video.
How to use this file
Use an example MP4 to test container demuxing, track and codec detection, seeking and streaming, and transcoding or thumbnail-extraction pipelines.
How to use this file for testing
“Deblur Input — detail-chart, Defocus blur, radius 5” 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-defocus.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.