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I did research on a new dataset for image quality assessment: Hence, the more similar between prediction and reference, the better the performance of the modelĪfter having these two metrics, I re-evaluate the function model of NIMA: The checkpoint given by the paper still achieves the best performance: 0.589 on the F1 score for the class of digiKam.Īs I explained last week, we need a balanced dataset that the score is evenly distributed. Spearman’s rank correlation coefficient is a metric to evaluate the similarity between 2 distributions.As most of the images are in class pending, the data is still imbalanced. As our use-case at the end is the classification between 3 classes: rejected, pending, and accepted image, hence, I split the data into three classes with the same meaning.Change metric that considers more weighted on the result of different region scoreĪs described in the last post, the current metric for regression (MSE) is not suitable for imbalanced data.Augment data of different ranges / reduce data of concentrated range to have a balance dataset.These two images show the distribution of the score on 2 datasets: AVA and EVA:įigure1 : distribution of score of AVA datasetįigure2 : distribution of score of EVA dataset The metric MSE will show that it is a accepted model even when the model will predict all the images with a score of 5. It creates the problem of evaluating the model.

This is reasonable as if the annotator can not be sure about the quality of the image, they will give a score of 4, 5, or 6.

Most images are labeled with a score of around 5.

Using the pre-trained model of the paper achieves a good performance: MSE = 0.3107 (means the variance is 3.1 on the scale score of IQS digiKam).

For now, I can train, evaluate and predict each image using python.

Adapt the label of 2 datasets for the context of NIMA.

Write training and testing script for NIMA.

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Download dataset of EVA and AVA datasets.

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Install environment for running python code of NIMA and musiq.

These are my principal task in my first week : All is published in my own repose iqs digikam. Hence, only the code needed for these tasks is extracted from their repos. I aim to test and train the deep learning model following the algorithms. First week - Īs described in the proposal, the first week is dedicated to experimenting and reproducing the result of two target algorithms NIMA and musiq. Therefore, we can reduce most of the fine-tuning parts to make this feature easier to useĬheck out the IQS Proposal for more info about the description and algorithm of the project. As deep learning is an end-to-end solution, it doesn’t require the setting for the hyperparameter.

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While the dataset and the paper for aesthetic image quality assessment are free to use, we are capable of constructing a mathematical model that can learn the pattern of a dataset, hence, predicting the score of quality. So, I propose the solution of the deep learning algorithm. However, the current algorithm also presents some drawbacks: It demands lots of fine-tuning from the user’s side and cannot work on the aesthetic image. The current approach helps determine whether images are distorted for one of these reasons. This score is based on four factors sabotaging image: blur, noise, exposure, and compression. The current version is 3.8.1.The main idea of IQS in digiKam is to determine the quality of an image and convert it into a score. Then darktable can still be used to keep those photos in the DAM and they can be further edited using the non-destructive editor. You could think about using darktable and for some photos sending them to DXO if they are very noisy and you want to take advantage of the very good DXO DeepPrime NR. Of course, you would benefit a lot by skimming the online manual.

Some years ago darktable was somewhat confusing and harder to use, but these days I have found that there have been so many improvements that it is much better in that regard and it is free. Of course, darktable and all of these programs have some other functions also, but all of them are primarily programs that have those 3 most important, basic functions. Other programs that do those 3 things are Lightroom Classic, ACDSee Ultimate/Pro, On1 Photo Raw, and Exposure X7. You could also consider the free, powerful darktable which is a: You might investigate the free, powerful digikam DAM.

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