Introduction to Image Quality Assessment

Outline Applications Image Quality Assessment Image Quality Assessment with Reference Image -Zhou Wang and Alan C. Bovik, ICASSP2002 Blind Image Quality Assessment -Xin Li, ICIP2002

Image Quality ?

Image Quality Assessment Good Bad

Applications Image Acquisition Systems and Display Systems Image Processing Systems and Algorithms Compression and Network

Image Quality Assessment Mean Opinion Score Automatically Image Quality Evaluation

Mean-Squared Error and Peak Signal-to-Noise Ratio

Frequency-domain SNR

Frequency-domain SNR

Image Quality Assessment Methods Image Quality Assessment with Reference Image Blind Image Quality Assessment

Image Quality Assessment Methods Image Quality Assessment with Reference Image Blind Image Quality Assessment

Error Sensitivity Based Image Quality Measurement

Error Sensitivity Based Image Quality Measurement alignment, luminance transformation, and color transformation

Error Sensitivity Based Image Quality Measurement resulting in two sets of transformed signals for different channels

Error Sensitivity Based Image Quality Measurement The errors between the two signals in each channel are calculated and weighted, usually by a Contrast Sensitivity Function (CSF).

Error Sensitivity Based Image Quality Measurement The weighted error signals are adjusted by a visual masking effect model, which reflects the reduced visibility of errors presented on the background signal

Visual Masking Effect

Error Sensitivity Based Image Quality Measurement Minkowski error pooling

Weaknesses of Error Sensitivity Based Methods The reference signal is of perfect quality There exist visual channels in the HVS and the channel responses can be simulated by an appropriate set of channel transformations. CSF variance and intra-channel masking effects are the dominant factors that affect the HVS’s perception on each transformed coefficient in each channel OK!

Weaknesses of Error Sensitivity Based Methods For a single coefficient in each channel, after CSF weighting and masking, the relationship between the magnitude of the error and the distortion perceived by the HVS can be modeled as a non-linear function. The interaction between channels is small enough to be ignored.

Weaknesses of Error Sensitivity Based Methods The perceived image quality is determined in the early vision system. Higher level processes, such as feature extraction, pattern matching and cognitive understanding happening in the human brain, are less effective Active visual processes, such as the change of fixation points and the adaptive adjustment of spatial resolution because of attention, are less effective

Structure Distortion Based Image Quality Measurement The main function of the human eyes is to extract structural information from the viewing field, and the human visual system is highly adapted for this purpose. Therefore, a measurement of structural distortion should be a good approximation of perceived image distortion

Structure Distortion Based Image Quality Measurement

Structure Distortion Based Image Quality Measurement loss of correlation, mean distortion, and variance distortion

Experimental Results a: original b: 0.9372 c: 0.3891 d: 0.6494 f: 0.2876

Image Quality Assessment Methods Image Quality Assessment with Reference Image Blind Image Quality Assessment

Blind Image Quality Assessment Human visual system usually does not need any reference to determine the subjective quality of a target image Distinction between fidelity and quality

Blind Image Quality Assessment Edge Sharpness Level Random Noise Level Structured Noise Level

Edge Sharpness Level

Random Noise Level Impulse Noise Additive White Gaussian Noise

Structured Noise Level Block Artifact Ringing Artifact

Blind Image Quality Assessment Combination of different measurement is still a problem.

Conclusion Image Quality Assessment with Reference Image Blind Image Quality Assessment