Deep Learning for Objective Quality Assessment of Tone Mapped Images

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About Course

This course, titled “Deep Learning for Objective Quality Assessment of Tone Mapped Images,” is designed to provide a comprehensive understanding of how deep learning techniques can be applied to evaluate the quality of tone-mapped images. High dynamic range (HDR) imaging captures real-world luminance values that cannot be directly displayed on standard screens, necessitating tone mapping to transform HDR content into low dynamic range (LDR) for display. Tone mapping algorithms aim to preserve the naturalness and structural details of the original images, but their performance can vary significantly depending on the content and the specific algorithm used. Subjective evaluations, where participants rank or score tone-mapped images based on their preferences, are time-consuming and impractical for every new image and algorithm. Therefore, objective metrics are crucial for efficiently assessing image quality. This course will guide you through the development of a robust objective metric using deep learning, leveraging a custom dataset of tone-mapped images and comparing the proposed metric against existing state-of-the-art methods.
 
The course is structured into several modules that cover the fundamentals of HDR imaging and tone mapping, the basics of deep learning, dataset preparation, model building and training, model evaluation, and practical applications. You will learn how to design and implement a deep learning model for objective quality assessment, fine-tune it for optimal performance, and evaluate its effectiveness using various metrics. Practical examples and case studies will help you understand how to apply these techniques in real-world scenarios, such as medical imaging and video game development. By the end of the course, you will have the skills to develop, train, and evaluate deep learning models for image quality assessment, making you well-equipped to contribute to this exciting field of research and application.
 
Abstract:

High dynamic range (HDR) images capture real-world luminance values which cannot be directly displayed on the screen and require tone mapping to be shown on low dynamic range (LDR) hardware. During this transformation, tone mapping algorithms are expected to preserve the naturalness and structural details of the image. In this regard, the performance of a tone mapping algorithm can be evaluated through a subjective study where participants rank or score tone mapped images based on their preferences. However, such subjective evaluations can be time-consuming and cannot be repeated for every tone mapped image. To address this issue, numerous quantitative metrics have been proposed for objective evaluation. This paper presents a robust objective metric based on deep learning to quantify image quality. We assess the performance of our proposed metric by comparing it to 20 existing state-of-the art metrics using two subjective datasets, including one benchmark dataset and a novel proposed dataset of 666 tone mapped images comprising a variety of scenes and labeled by 20 users. Our ap proach exhibits the highest correlation with subjective scores in both evaluations, confirming its effectiveness and potential to be a reliable alternative to laborious subjective studies.

DOI:

https://www.nowpublishers.com/article/Details/SIP-20240007

Cite: 

https://www.nowpublishers.com/article/OpenAccessDownload/SIP-20240007

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What Will You Learn?

  • Understand the principles and applications of High Dynamic Range (HDR) imaging, which captures a wider range of luminance values than standard imaging.
  • Explore the necessity of tone mapping to convert HDR images to low dynamic range (LDR) for display on standard screens, and learn about various tone mapping operators (TMOs).
  • Gain a foundational understanding of neural networks and their role in deep learning.
  • Learn about Convolutional Neural Networks (CNNs), a powerful type of neural network used extensively in image processing and computer vision.
  • Understand the process of training deep learning models, including data preparation, loss functions, and optimization algorithms.
  • Learn about the characteristics of a good dataset and why high-quality data is crucial for training effective deep learning models.
  • Explore techniques for augmenting your dataset to improve model robustness and generalization.
  • Gain access to and learn how to use a custom dataset of tone-mapped images for training and evaluation.
  • Learn how to design a deep learning model architecture suitable for image quality assessment.
  • Gain hands-on experience implementing the model using popular deep learning frameworks like TensorFlow/Keras.
  • Understand the importance of hyperparameters and how to fine-tune them for optimal model performance.
  • Learn how to set up your environment for training deep learning models, including installing necessary libraries and configuring the training pipeline.
  • Understand how to monitor the training process and adjust parameters like learning rate and batch size.
  • Learn how to evaluate the performance of your model using metrics like Mean Squared Error (MSE) and correlation coefficients (Pearson, Spearman, Kendall).
  • Explore practical applications of deep learning in image quality assessment, including medical imaging and video game development.
  • Dive into advanced topics such as transfer learning and multi-task learning.
  • Develop and present a custom image quality assessment metric, applying the skills and knowledge gained throughout the course.
  • Stay updated with the latest trends and emerging technologies in deep learning and image quality assessment.
  • Gain insights into potential research opportunities and future directions in the field.

Course Content

Module 1: Introduction to HDR Imaging and Tone Mapping
1.1 Introduction to High Dynamic Range (HDR) Imaging What is HDR? Applications of HDR Imaging 1.2 Understanding Tone Mapping Why Tone Mapping is Necessary Types of Tone Mapping Operators (TMOs) 1.3 Overview of Image Quality Assessment (IQA) Subjective vs. Objective IQA Challenges in Evaluating Tone Mapped Images

Module 2: Deep Learning Basics
2.1 Introduction to Deep Learning Basics of Neural Networks Convolutional Neural Networks (CNNs) 2.2 Training Deep Learning Models Data Preparation and Preprocessing Loss Functions and Optimization Algorithms 2.3 Evaluating Deep Learning Models Metrics for Model Evaluation Overfitting and Underfitting

Module 3: Dataset Preparation
3.1 Importance of Datasets in Deep Learning Characteristics of a Good Dataset 3.2 Preparing the Dataset for Tone Mapped Images Collecting and Labeling Data Data Augmentation Techniques 3.3 Introduction to the Proposed Dataset Overview of the Custom Dataset Accessing and Using the Dataset

Module 4: Building the Deep Learning Model
4.1 Designing the Model Architecture Choosing the Right Architecture Convolutional Layers and Max-Pooling 4.2 Implementing the Model in Python Using TensorFlow/Keras Hands-on Coding Session 4.3 Fine-Tuning the Model Hyperparameter Tuning Regularization Techniques

Module 5: Training the Model
5.1 Setting Up the Training Environment Installing Required Libraries Configuring the Training Pipeline 5.2 Training the Model Monitoring Training Progress Adjusting Learning Rate and Batch Size 5.3 Saving and Loading the Model Best Practices for Model Management

Module 6: Evaluating the Model
6.1 Evaluating Model Performance Using Mean Squared Error (MSE) Correlation Metrics (Pearson, Spearman, Kendall) 6.2 Comparing with Existing Metrics Benchmarking Against State-of-the-Art Methods Case Studies and Practical Examples 6.3 Interpreting Results Understanding Model Outputs Visualizing Predictions

Module 7: Practical Applications and Case Studies
7.1 Applying the Model to Real-World Problems7.2 Advanced Topics in Deep Learning for IQA7.3 Hands-on Project

Module 8: Conclusion and Future Work
8.1 Summary of Key Concepts Recap of HDR Imaging, Tone Mapping, and Deep Learning 8.2 Future Directions in Deep Learning for IQA Emerging Trends and Technologies 8.3 Course Wrap-Up and Q&A Final Thoughts and Open Discussion

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