bubu_ka's profile - activity

python: how to compute the color distribution of image in the paper with python and openCV I am computing the color dist

thanks for your comments, how to compute it with python.

python: how to compute the sharpness features of image I am extracting the sharpness features of image as shown in the f

@berak could you tell me your comment in detail. what's local vs global mean value ?

python: how to compute the sharpness features of image I am extracting the sharpness features of image as shown in the f

python: how to compute the sharpness features of image I am extracting the sharpness features of image as shown in the f

Hi I am extracting the grey level features of image mentioned in the following paper: part 4.5 low level features: https://www.cs.cmu.edu/~yke/photoqual...

We describe two low level features that are particu- larly important for photo quality assessment – contrast and brightness. Professional photos usually have higher contrast than snapshots. We measure the contrast of an image as fol- lows. First, we compute the gray level histogram Hr , Hg , and Hb for each of the red, green, and blue channels, re- spectively. Then, we compute the combined histogram H, where

I can't clearly understand the above part in the paper. As my understanding about grey level features, take the above image as example. Use the opencv convert the color image to grey image. as the following code:

image = cv2.imread('lenna.png') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

and then using the following code to get the histogram with the following code:

hist = cv2.calcHist([gray], [0], None, [256], [0,255])

I want to ask, and I don't know the meaning of gray level histogram for each channel.

(1) how to we compute the gray level histogram Hr , Hg , and Hb for each of the red, green, and blue channels.

(2) how to compute the the contrast quality, qct mentioned in the paper.

as the above comment, as the number of dominant b ins in the grey level histogram, what's grey level histogram, is the

as the above comment, as the number of dominant b ins in the grey level histogram, what's grey level histogram, is the

hi @berak , I understand your answer. actually I am computing the features(3.1 gray level features) in the following pa

I wan to confirm: based on your method(code), the q1: 248 , q0 is 0 for the above image. The H[0] is 0.104 bigger than 0

thanks vey much for your answer, it is really helpful for me.

python: how to compute the gray level histogram features as mentioned in the paper Hi I am extracting the grey level fea