1 INTRODUCTION
1.1 Organization

2 BACKGROUND
2.1 Deep Neural Network
2.2 Image Quality Metrics

3 A TYPICAL PIPELINE

4 DATASETS

5 CONVENTIONAL APPROACHES WITH HAND-CRAFTED FEATURES
5.1 Simple Image Features
5.2 Image Composition Features
5.3 General-Purpose Features
5.4 Task-Specific Features

6 DEEP LEARNING APPROACHES
6.1 Generic Deep Features

7 EVALUATION CRITERIA AND EXISTING RESULTS

8 EXPERIMENTS ON DEEP LEARNING SETTINGS
8.1 Formulation and the Base CNN Structure
8.2 Training From Scratch vs Fine-Tuning
8.3 Mini-Batch Formation
8.4 Triplet Pre-Training and Multi-Task Learning
8.5 Multi-Column Deep Architecture
8.6 Model Depth and Layer-wise Effectiveness
8.7 From Generic Aesthetics to User-specific Taste

9 IMAGE AESTHETIC MANIPULATION
9.1 Aesthetic-based Image Cropping
9.2 Plausible Formulations based on Deep Models

10 CONCLUSION AND POTENTIAL DIRECTIONS