Image quality measures Pasi Frnti 2 3 2016

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Image quality measures Pasi Fränti 2. 3. 2016

Image quality measures Pasi Fränti 2. 3. 2016

Pixelwise measures

Pixelwise measures

Classical measures Mean squared error (MSE): Peak Signal to Noise ratio (PSNR): Original pixel

Classical measures Mean squared error (MSE): Peak Signal to Noise ratio (PSNR): Original pixel values are xi and distorted xi. Maximum pixel value is 255.

Blockwise measure

Blockwise measure

Blockwise distortion measure Signal Processing: Image Communication, 13 (2), 89 -98, 1998

Blockwise distortion measure Signal Processing: Image Communication, 13 (2), 89 -98, 1998

Three quality factors Contrast errors: Structural errors: Quantization errors:

Three quality factors Contrast errors: Structural errors: Quantization errors:

Weighting the factors Weighted sum: Scaling function: Scales the values approximately to the range

Weighting the factors Weighted sum: Scaling function: Scales the values approximately to the range 0, 1. Negative values are rounded to zero.

Test images Lena (512 512) Airplane (512 512) Bridge (256 256)

Test images Lena (512 512) Airplane (512 512) Bridge (256 256)

Test arrangements Image samples: Offset quality photographs Viewing conditions: Normal office environment Viewing distance:

Test arrangements Image samples: Offset quality photographs Viewing conditions: Normal office environment Viewing distance: Free of choice for the observer Time of evaluation: Unlimited Number of observers: 15 -39 Grading: Extended MOS scale (0 -10).

Visual examples JPEG (1. 0) BTC QT

Visual examples JPEG (1. 0) BTC QT

JPEG Contrast: Structural: Quantization: BTC QT

JPEG Contrast: Structural: Quantization: BTC QT

Scatter of the quality grades

Scatter of the quality grades

Quality grades

Quality grades

Correlation to subjective quality

Correlation to subjective quality

Conclusions • Blockwise measure instead of pixelwise like MSE or PSNR • Correlates better

Conclusions • Blockwise measure instead of pixelwise like MSE or PSNR • Correlates better to subjective quality • Old method but worth to study further: – Extension to Multi-scale resolution – Study with newer image datasets – Compare to SSIM 1 and RF 2 1 W. Zhou, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli Image quality assesment: from error visibility to structure similarity IEEE Trans. on Image Processing, 13 (4), 600 -612, 2004. 2 A. Mahmoudi-Aznaveh, A. Mansouri, F. Torkamani-Azar, and M. Eslami Image Quality Measurement Besides Distortion Type Classifying Optical Review, 16 (1), 30 -34, 2009.