example application : dave gibson's medical image video
TRANSCRIPT
Video Compression for Medical Imaging
by David Gibsonby David Gibson
Contents Part 1: Compression BackgroundPart 1: Compression Background
Fundamentals of CompressionFundamentals of Compression Video & Motion CompensationVideo & Motion Compensation
Part 2: Medical ImagingPart 2: Medical Imaging Example of the data + JPEG /Wavelet encodingExample of the data + JPEG /Wavelet encoding Motion compensationMotion compensation Region of interest (ROI) codingRegion of interest (ROI) coding
Part 1
Video Compression ReviewVideo Compression Review
Foundations of Compression
The Foundations of Compression involves The Foundations of Compression involves looking at the data.looking at the data.
Foundations of Compression
Foundations of Compression
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DCT
Video Compression
Main Classification of Video Compression Methods
Intra-frame methodsIntra-frame methods
Uses single framesUses single frames e.g. MJPEG - JPEG e.g. MJPEG - JPEG
applied to videoapplied to video
Inter-frame methodsInter-frame methods
Uses temporal Uses temporal informationinformation
e.g. MPEG-1/2, H.263e.g. MPEG-1/2, H.263 Usual approach to Usual approach to
video compressionvideo compression
Inter-frame methods
Use Motion CompensationUse Motion Compensation
Motion Compensation
Exploitation of temporal redundancy.Exploitation of temporal redundancy.
Frame 30 Frame 31
Motion Compensation
How Do We Motion Compensate? Compensate each pixel separately with its own Compensate each pixel separately with its own
motion vector?motion vector?
Huge amount of motion data - More data than the Huge amount of motion data - More data than the original image!original image!
Can’t afford to motion compensate each Can’t afford to motion compensate each individual pixel.individual pixel.
ErrorData
MotionData
Solution
One motion vector for a group of pixels.One motion vector for a group of pixels. Based on looking at the data.Based on looking at the data.
Block Matching
Foundation of most current video coders Foundation of most current video coders (MPEG 1/2, H.261/3).(MPEG 1/2, H.261/3).
Conclusions (part 1)
Presented a brief summary of video Presented a brief summary of video compression methodscompression methods
Part 2
Video Compression of Medical Video Compression of Medical ImagesImages
Medical Imaging
Angiogram Video;Angiogram Video; Pictures taken of the heart at 30 frames/secondPictures taken of the heart at 30 frames/second 512x512 images - 8 bits/pixel512x512 images - 8 bits/pixel Typical procedure - 5 minutesTypical procedure - 5 minutes
Resulting in 2.5GBytes of data per patient.Resulting in 2.5GBytes of data per patient. @64Kbits/sec - 80 hours.@64Kbits/sec - 80 hours. @10Mb/sec - 30 minutes.@10Mb/sec - 30 minutes.
Summary
Going to look at 3 aspects of the research Going to look at 3 aspects of the research we’ve been doing:we’ve been doing: Example of the data + JPEG/Wavelet encodingExample of the data + JPEG/Wavelet encoding Motion compensationMotion compensation Region of interest (ROI) codingRegion of interest (ROI) coding
Example Angiogram Sequence
Example JPEG Coding
Still Frame Coding Methods : Wavelet
Similar Similar frequency approachfrequency approach to DCT. to DCT. But considered to give better results.But considered to give better results. Operation on the whole image.Operation on the whole image.
JPEG/Wavelet Comparison
0.2 0.4 0.6 0.8 1 1.20
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Resultant Bit Rate (Bits/Pixel)
RM
S D
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rtio
nDCT Wavelet
Single Frame0
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RM
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Inter-frame Prediction
Use an ‘off the shelf’ video coder?
Typical results for an Typical results for an angiogram image angiogram image @[email protected].
Comparison of intra- Comparison of intra- and inter-frame methods and inter-frame methods using DCT.using DCT.
Motion compensation performs badly for this Motion compensation performs badly for this type of data.type of data.
Key Point: Compression effectiveness Key Point: Compression effectiveness depends upon the datadepends upon the data
Motion Compensation - Failure?
Conventional motion compensation assumptionsConventional motion compensation assumptions:: Distinct, opaque objects moving simply.
Also, angiogram images contain high frequency uncorrelated texture.
Motion Compensation - Failure?
Objects in angiograms are partially Objects in angiograms are partially transparent.transparent.
Image is made up of several Image is made up of several layerslayers of bones of bones and tissue, all moving differently.and tissue, all moving differently.
Conventional motion compensation model Conventional motion compensation model doesn’t apply well.doesn’t apply well.
Region of Interest (ROI) Coder
Aim is to shift the allocation of bits from Aim is to shift the allocation of bits from uninteresting areas of the image to more uninteresting areas of the image to more interesting ones.interesting ones.
Makes more efficient use of the available Makes more efficient use of the available bits.bits.
ROI Example : Simple Case
Manual segmentation.Manual segmentation.
ROI non-ROI
Example ROI coder
Example of transferring bits from non-ROI Example of transferring bits from non-ROI to ROIto ROI
ROI : Simple Case - Results
Much lower error in the ROI at the expense Much lower error in the ROI at the expense of the non-ROI.of the non-ROI.
0 0.5 1 1.5 2 2.5 3 3.5 40
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Rate (bits/pixel)
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tort
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(RM
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rror
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RD Graph with ROI - DFD Data (Global MC - M.Black)
No ROI (baseline comparison)ROI Distortion Non-ROI Distortion
Key Aim
Reallocate bits from diagnostically Reallocate bits from diagnostically unimportant areas into diagnostically unimportant areas into diagnostically interesting onesinteresting ones
Eye Tracking (proof of concept) Experiment to identify Experiment to identify
key areas of an key areas of an angiogram image.angiogram image.
Example Results (Expert)
Example Results (Sandra)
Eye Tracking
Significant areas of Significant areas of the image are not the image are not directly examined.directly examined.
Methods of measuring image quality:Methods of measuring image quality: Classical RMS - Measure of intensity level Classical RMS - Measure of intensity level
difference for each pixel.difference for each pixel. Perceptual measure - Takes in to account the Perceptual measure - Takes in to account the
observer.observer.
Quality Measure and Results
Quality Measure and Results
Perceptual Perceptual measurement of measurement of image quality.image quality.11 22 33 44 55
PoorPoor PerfectPerfect
OriginalOriginal CompressedCompressed
What’s next for video compression research?
More efficient compression methods - to More efficient compression methods - to better take advantage of data (e.g. object better take advantage of data (e.g. object based)based)
Perceptual coding - introducing the viewer Perceptual coding - introducing the viewer into the equationinto the equation
Questions?