Sampling in 1D ( ) Continuous time signal f(t) Discrete time signal. f(t) comb

Transcription

1 Sampling in 2D 1

2 Sampling in 1D Continuous time signal f(t) Discrete time signal t ( ) f [ k] = f( kt ) = f( t) δ t kt s k s f(t) comb k 2

3 Nyquist theorem (1D) At least 2 sample/period are needed to represent a periodic signal 1 2π Ts 2 ωmax 2π ωs = 2ω T s max 3

4 Delta pulse 4

5 Dirac brush 5

6 Comb 6

7 Brush 7

8 Sampling in p-dimensions Nyquist theorem 2D spatial domain T s x s f T T ( x) = δ ( x kt) p k Z ( x) = f ( x) s ( x) T T s y 2D Fourier domain Nyquist theorem ω y 1 T 2π ω ω ω ω s s x x 2 xmax 2 xmax s 2 1 y ωymax s Ty 2π 2ω y max ω ymax ω xmax ω x 8

9 Spatial aliasing 9

10 Resampling Change of the sampling rate Increase of sampling rate: Interpolation or upsampling Blurring, low visual resolution Decrease of sampling rate: Rate reduction or downsampling Aliasing and/or loss of spatial details 10

11 Downsampling 11

12 Upsampling nearest neighbor (NN) 12

13 Upsampling bilinear 13

14 Upsampling bicubic 14

15 Quantization 15

16 Scalar quantization A scalar quantizer Q approximates X by X =Q(X), which takes its values over a finite set. The quantization operation can be characterized by the MSE between the original and the quantized signals Suppose that X takes its values in [a, b], which may correspond to the whole real axis. We decompose [a, b] in K intervals {( y k-1, y k ]} 1 k K of variable length, with y 0= a and y K =b. A scalar quantizer approximates all x ( y k-1, y k ] by x k : ( ] ( ) x y,, 1 y Q x = x k k k 16

17 Scalar quantization The intervals (y k-1, y k ] are called quantization bins. Rounding off integers is an example where the quantization bins qui have size 1and x k =k for any k Z. (y k-1, y k ]=(k-1/2, k+1/2] High resolution quantization Let p(x) be the probability density of the random source X. The mean-square quantization error is 17

18 HRQ A quantizer is said to have a high resolution if p(x) is approximately constant on each quantization bin. This is the case if the sizes k are sufficiently small relative to the rate of variation of p(x), so that one can neglect these variations in each quantization bin. p(x) Δp(x) HRQ: Δp(x) 0 0 x Δ k 18

19 Scalar quantization Teorem 10.4 (Mallat): For a high-resolution quantizer, the mean-square error d is minimized when x k =(y k +y k+1 )/2, which yields d K 1 = pkδ 12 k = 1 2 k 19

20 Uniform quantizer 20

21 Quantization A/D conversion quantization f in L 2 (R) Quantizer discrete function f in L 2 (Z) X=Q{y} uniform f q =Q{f} perceptual r k y k y k+1 y f The sensitivity of the eye decreases increasing the background intensity (Weber law) 21

22 Quantization Signal before (blue) and after quantization (red) Q Equivalent noise: n=f q -f additive noise model: f q =f+n 22

23 Quantization original 5 levels 10 levels 50 levels 23

24 Distortion measure D = Ε Distortion measure K t k [( f Q f ) ] = ( f Q f ) k = 0 The distortion is measured as the expectation of the mean square error (MSE) difference between the original and quantized signals. Lack of correlation with perceived image quality Even though this is a very natural way for the quantification of the quantization artifacts, it is not representative of the visual annoyance due to the majority of common artifacts. Visual models are used to define perception-based image quality assessment metrics t k i 2 p( 1 j 1 f ) df PSNR = 20log10 = 20log10 MSE N M 1 N M = = ( I [ i, j] I [ i, j] )

25 Example The PSNR does not allow to distinguish among different types of distortions leading to the same RMS error between images The MSE between images (b) and (c) is the same, so it is the PSNR. However, the visual annoyance of the artifacts is different 25

26 Convolution 26

27 Convolution ct () = f () t g () t = f ( τ ) g( t τ) dτ cn [ ] = f [ n] g[ n] = f [ k ] g[ k n] + + k = 27

28 2D Convolution c( x, y) = f ( x, y) g( x, y) = f ( τ, ν) g( x τ, y ν) dτdν + + cik [, ] = f[ nmgi, ] [ nk, m] n= m= + + filter impulse response rotated by 180 deg Associativity Commutativity Distributivity [n,m] 28

29 + + cik [, ] = f[ nmgi, ] [ nk, m] n= m= 2D Convolution f(n,m) g(n,m) m m n n 1. fold about origin 2. displace by i and k g(i-n,k-m) 3. compute integral of the box f(n,m) g(i-n,k-m) k i Tricky part: borders (zero padding, mirror...) 29

30 Filtering with filter h(x,y) Convolution sampling property of the delta function 30

31 Convolution Convolution is a neighborhood operation in which each output pixel is the weighted sum of neighboring input pixels. The matrix of weights is called the convolution kernel, also known as the filter. A convolution kernel is a correlation kernel that has been rotated 180 degrees. Recipe 1. Rotate the convolution kernel 180 degrees about its center element. 2. Slide the center element of the convolution kernel so that it lies on top of the (I,k) element of f. 3. Multiply each weight in the rotated convolution kernel by the pixel of f underneath. Sum the individual products from step 3 zero-padding is generally used at borders but other border conditions are possible 31

32 f = [ ] kernel h = [ ] Example h = [ ] 32

33 Correlation The operation called correlation is closely related to convolution. In correlation, the value of an output pixel is also computed as a weighted sum of neighboring pixels. The difference is that the matrix of weights, in this case called the correlation kernel, is not rotated during the computation. Recipe 1. Slide the center element of the correlation kernel so that lies on top of the (2,4) element of f. 2. Multiply each weight in the correlation kernel by the pixel of A underneath. 3. Sum the individual products from step 2. 33

34 f = [ ] kernel h = [ ] Example 34