current trends in image quality perception
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Current Trends in Image Quality Perception. Mason Macklem Simon Fraser University http://www.cecm.sfu.ca/~msmackle. General Outline. Examine model of human visual system (HVS) Examine properties of human perception of images consider top-down/bottom-up distinction - PowerPoint PPT PresentationTRANSCRIPT
Current Trends in Image Quality Perception
Mason Macklem
Simon Fraser University
http://www.cecm.sfu.ca/~msmackle
General Outline
• Examine model of human visual system (HVS)
• Examine properties of human perception of images– consider top-down/bottom-up distinction
• Discuss combinations of current models, based on different perceptual phenomena
Quality-based model
• Pros:– Very nice theoretically
– Clearly-defined notions of quality
– Based on theory of cognitive human vision
– Flexible for application-specific model
• Cons:– Practical to
implement?
– Subject-specific definition of quality
– Subjects more accurate at determining relative vs. absolute measurement
Perception vs Semantic Processing
• Based on properties of HVS
• Models eye’s reaction to various stimuli– eg. mach band, sine
grating, Gabor patch
• Assumes linear model to extend tests to complex images
• Based on properties of Human Attention
• Models subjects’ reactions to different types of image content– eg. Complex, natural
images
• Bypasses responses to artificial stimuli
Human Visual System Model
• Breaks process of image-processing into interaction of contrast information with various parts of the eye
• Motivates representation by discrete filters
• Cornea and lens focus light onto retina
• Retina consists of millions of rods and cones– rods: low-light vision
– cones: normal lighting
– rods:cones => 60:1
• Fovea consists of densely packed cones– processing focusses on
foveal signals
Motivation for Frequency Response Model
• Errors in image reconstruction are differences in pixel values– Interpreted visually as differences in luminance
and contrast values (ie. physical differences)
• Model visual response to luminance and localized contrast to predict visible errors– assuming linear system, measurable using
response to simple phenomena
Contrast Sensitivity Function (CSF)
• increasing frequency levels can be resolved to limited extent
• CSF: represents limitations on detecting differences in increasing frequency stimuli– specific to given lens and viewing conditions
• Derive by capturing images for increasing frequency gratings
Cortex Transform
• Used to simulate sensitivity of visual cortex to orientation and frequency
• Splits frequency domain into 31 (?) sections, each of which is inverse transformed separately
Masking Filter• Nonlinear filter to simulate masking due to local
contrast– function of background contrast
• Masking calculated separately using reactions to sine grating and Gaussian noise
• Uses learning model to simulate prediction of background noise– similar noise across images lessens overall effect
Probability Summation• Describes the increase in the probability of
detection as the signal contrast increases• Calculates contrast difference between the two
images, for each of the (31) images• In most cases, the signs will agree in every pixel
for each cortex band – use the agreed sign as the sign of the probability
• Overall probability is product over all (31) cortex transformed images
• See book for example of Detection Map
Bottom-up vs. Top-down
• Stimulus driven– eg. Search based on
motion, colour, etc.
• Useful for efficient search
• Attracted to objects rather than regions– attention driven by
object properties
• Task/motivation-based– eg. Search based on
interpreting content
• Not as noticeable during search
• Motivation-based search still shows effects of object properties
Saccades & Drifts• Rapid eye movements
– occur 2-3 times/second
• HVS responds to changes in stimuli
• Saccades: search for new ROI, or refocus on current ROI
• Drifts: slow movement away from centre of ROI to refresh image on retina
Veronique
Ruggirello
Influences of Visual Attention
• Measured with visual search experiments– subjects search for target item from group
– target item present in half of samples
• Two measures:– Reaction Time: time to find object correctly vs. number
of objects in set
– Accuracy: frequency of correct response vs. display time of stimulus
• Efficient test: reaction time independent of set size
Other features
• Color: – EOS will increase as color-difference increases– Eg. Levi’s patch on jeans
• Edges:– Edges attended more than textured regions
• Predictability:– Attention directed towards familiar objects
• Motion:– EOS will increase as motion-difference increases
Region-of-Interest Importance Map (ROI)
• Visual attraction directed to objects, rather than regions
• Treats image as a collection of objects– Weights error w/i objects according to various
types of attentive processes
• Results in Importance Map– Weights correspond to probability that location
will be attended directly
Notes on ROI• VDP Detection Map: probability that existing
pixel differences will be detected• ROI Importance Map: probability that existing
visible pixel differences will be attended• Overall probability of detection should be a
combination of both factors• Open question: single number for either model?