基于matlab CCF算法的图像质量评价

haidaowang

源代码

8 ~- Y2 o  [+ a: `5 I``
clear all
clc
& H% t4 w* s9 ?' w) f1 p6 s
im = imread(‘1.bmp’);
2 n( Q; s) g+ v3 M3 z5 [: f
! X2 r3 c5 g: i3 N) V2 x+ t3 ~7 \quality = CCF(im)
" a6 F2 I/ ~1 {( K, h, xfunction [quality] = CCF(im)
0 X, ~" X3 E+ V1 _* ]) H: ^
%-------------CCF_coloRFulness -----------------
+ X' c8 \# l/ W: a$ M1 j3 NimColor = double(im);
0 K7 \8 N5 ?' W4 Y' S+ r
* k7 _) r+ G& ^* m, ?* q
: j+ M& G. L) e* o' A7 `

• R = imColor(:,:,1);
• G = imColor(:,:,2);
• B = imColor(:,:,3);
• RR = log(R+0.00001) - mean2(log(R+0.00001));
• GG = log(G+0.00001) - mean2(log(G+0.00001));
• BB = log(B+0.00001) - mean2(log(B+0.00001));
• alpha = RR-GG;
• beta = 0.5*(RR+GG)-BB;
• mu_alpha=mean(mean(alpha));
• mu_beta=mean(mean(beta));
• var_alpha=var(var(alpha));
• var_beta=var(var(beta));
• CCF_colorfulness=1000*((sqrt(var_alpha+var_beta)+0.3*sqrt(mu_alpha*mu_alpha+mu_beta*mu_beta))/85.59);
  

/ A% c- ?1 P5 s  C: m' K
%-------------CCF_contrast----------------------

3 S+ T9 L; h* [$ {: \8 \; j- ]5 m

• im1=rgb2gray(im);
• CCF_contrast = CCFcontrast(im1);
  

, ^2 p3 {/ Q+ q5 A3 c
: F5 @$ g* V2 G0 Z" {" j* S1 {& \%-------------CCF_FADE--------------------------

• CCF_FADE = FADE(im);
  $ o: X- q6 k, X8 q1 y

! t& ]. ~0 V& m' t% O' }
%------------ normalization ------------------
- O% M1 K' ?2 B( v; e) b

• CCF_colorfulness=mapminmax(CCF_colorfulness,1,10);
• CCF_contrast=mapminmax(CCF_contrast,1,10);
• CCF_FADE=10-mapminmax(CCF_FADE,1,10);
  1 M& c! c+ y* T( U1 ]9 @

* A+ a' v3 }, A& W* v$ U
% ------------calculate image quality with coefficients---------------------
) ^8 A% H! E& S1 [

• c=[0.17593 0.61759 0.33988 ];
• quality = c(1)*(CCF_colorfulness) + c(2)*(CCF_contrast) + c(3)*(CCF_FADE)
  9 S+ p& ?0 T; s' ?+ |6 m+ a

0 }% G& O  h. P' {& j6 }
end

" w  h0 k3 k8 K" I2 [: D, w
. [0 x' j) z& {; }4 y/ ?8 [

NNNei256

基于matlab CCF算法的图像质量评价

twel2e

可以借鉴一下
2 V% r$ ~1 a3 N0 P+ \
1 j. h& l. B9 D8 v, k) q