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This is the official repository for the Deep Banding Index paper Implementation. I worked on this project as a graduate researcher under Dr. Zhou Wang. The research project resulted is in ICASSP 2021 Conference paper on Banding, CNN, and Data Science for Image Processing.

Python 13.08% MATLAB 8.56% Makefile 0.83% HTML 11.26% Java 15.35% M4 9.62% C 10.93% C++ 14.80% Jupyter Notebook 15.57%
banding banding-data dataset deep-learning

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meng-699-image-banding-detection's Issues

Original videos in banding dataset

Hi,

Thanks for sharing this great work! I'm wondering could you share the original mp4 videos that you used for creating this dataset?

Variables dx and dy in Patches Generation code

Hi there,

I noticed a potential issue in the code snippet you provided from the file '/src/Generating_patches_from_HD_images.py'. It seems that there might be an error in assigning the variables dx and dy. The highlighted parts in the following code seem to have incorrect variable assignments: (line 56, 57)

dx = min(max(X1[1],X1[0]),max(X2[1],X2[1]))- max(min(X1[0],X1[0]),min(X2[0],X2[0]))
dy = min(max(Y1[1],Y1[0]),max(Y2[1],Y2[1]))- max(min(Y1[0],Y1[0]),min(Y2[0],Y2[0]))

I don't quite understand what's point of computing the parts in bold since the same elements were given either in max() or min(). Could you enlighten me here?

Based on my understanding and the solution mentioned in the link here, I believe the correct code should be:

dx = min(max(X1[0],X1[1]),max(X2[0],X2[1]))- max(min(X1[0],X1[1]),min(X2[0],X2[1]))
dy = min(max(Y1[0],Y1[1]),max(Y2[0],Y2[1]))- max(min(Y1[0],Y1[1]),min(Y2[0],Y2[1]))

Please review the modified code and let me know if my understanding aligns with your expectations. Feel free to correct me if I have misunderstood any aspect.


Update:

Upon further investigation, I realized that the outputs of max(X2[1], X2[1]) and (X2[0], X2[1]) are indeed both X2[1], and the same applies to other instances of max() and min(). Therefore, it appears that the code is correct in this regard. However, I have discovered another issue. The total number of generated banded patches (54,286) and nonbanded patches (155,639) does not match the total numbers mentioned in the paper (banded: 73,867 / nonbanded: 95,634).

Did I miss something?

Packages version error

Hi, I noticed an error when preparing the environment.

As mentioned here, tensorflow 2.1 require Python 3.4-3.7 but not higher.

I installed Python 3.8 and tried to pip install tensorflow-gpu==2.1
However, it returned ERROR: Could not find a version that satisfies the requirement tensorflow==2.1.0
I wonder how you installed Python 3.8 and tensorflow-gpu=2.1 at the same time. I would be appreciate if you can solve my concern!

About datasets (quantization process)

Hello, I'd like to ask if 'six level quantization' refers to quantization from 6 bits to 8 bits, or if it means six different quantization levels?

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