BLIND MEASUREMENT OF BLOCKING ARTIFACTS IN IMAGES Zhou Wang, Alan C. Bovik, and Brian L. Evans. (
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1 BLIND MEASUREMENT OF BLOCKING ARTIFACTS IN IMAGES Zhou Wang, Alan C. Bovik, and Brian L. Evans Laboratory for Image and Video Engineering, The University of Texas at Austin (
2
3 Introduction Block transform coding is widely adopted in most coding standards Blocking effect discontinuities along block boundaries Blocking artifact is very annoying to human eyes
4 Mean squared error (MSE) is not a good measure Blocking effect can be and should be measured blindly Human visual system (HVS) features need to be considered
5 Blocking Effect
6 Most image/video coding standards use block-based DCT Quantization is applied to DCT coefficients to achieve low bit rate Decoding is lossy Various decoding artifacts blocking, blurring, ringing, Blocking artifact is usually the most significant
7 Blocking Effect Measurement Automatic quantitative estimation of blocking effect Applications Quality assessment of compressed digital images and videos Evaluation of image and video coding systems
8 Encoder: optimize parameter selection and bit allocation Decoder: design post-processing algorithm Measurement Methods Mean squared error (MSE) Human visual system (HVS) based methods Blind measurement
9 Blind Measurement Human eyes can easily perceive blockiness without looking at the original image Original reference images are not available Especially useful for the design of post-processing algorithm at decoder
10 Previous method : Use weighted difference along block boundaries. Cannot distinguish blockiness and the oscillation in the original signal Sensitive to phase shift: Location of block boundaries must be exactly known
11 The Measurement System rows Differen -tiation. Masking FFT Power Spectrum Estimation vertical blocking measure test image Masker Evaluation Weighting & Summing blocking measure columns Differen -tiation. Masking FFT Power Spectrum Estimation horizontal blocking measure
12 Blocky Signal Modeling: Blocky signal = Original signal + Ideal blocky signal Measurement Detect and estimate the power of the blocky signal in the blocky image
13 Ideal Blocky Signal Ideal 1-D blocky signal Absolute difference signal
14 Magnitude of FFT result Phase of FFT result
15 Power Spectrum Analysis Power (log10(1+p[l])) Frequency (l/n) Power spectrum of the original image
16 1 0.8 Power (log10(1+p[l])) Frequency (l/n) Power spectrum of the blocky image
17 Power Spectrum Analysis (Cond.) Power (log10(1+p[l])) Frequency (l/n) Smoothed power spectrum of the blocky image
18 Power Spectrum P : P = { P[ l]; 0 l N / 2} Median Filtered Power Spectrum P M : P M [ l] = Median{ P[ l K], L, P[ l], L, P[ l + K ]} Smoothed Power Spectrum P M : P [ l] S = P[ l] l = PM [ l] N /8, 2N /8, 3N /8, otherwise or 4N /8
19 Blockiness Measure. Power of blockiness: M Bv = 4 i = 0 in ( P[ 8 ] P M in [ 8 ]) Energy of natural images highly concentrates on low frequency bands disturb the blockiness measure at 0 frequency.
20 To avoid disturbance and maintain total power, define: M Bv = i= 1 in ( P[ 8 ] P M in [ 8 ]) Overall Blockiness Measure average of horizontal and vertical measure: M = 0.5( M + M ) B Bv Bh
21 Masking Effect Masking Reduction in the visibility of one image component (the target) due to the presence of another Two kinds of masking effect texture masking and luminance masking
22 Our system is flexible to incorporate masking effect Masker evaluation results in a masker map for each pixel in the image The masker map is then used to scale the image. Other components of the system keep unchanged.
23 Results 20 JPEG compressed "Lena" image Proposed blocking measure Combining masking effects 15 Blocking measure Bit rate (bpp)
24 Blocking effect measurement result for JPEG compressed Lena image The blocking effect decreases very fast with increasing bit rate almost no visible blockiness at 0.5bpp or higher The same algorithm is applied to nonblocky images (e.g. wavelet coded images) zero blockiness
25 Conclusions and Future Work We propose a new blocking effect measurement algorithm with the following features: Blind measurement Can distinguish blockiness and discontinuities in the original signal
26 Insensitive to phase shift Future work Use higher-order statistics Fast implementation Combine with image and video coding algorithms Design post-processing algorithms
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