# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 2 Background
### Datasets and Assessment Metrics
#### 2.4.2 Assessment Metrics for Super-Resolved Images
\begin{table}
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**Name of Dataset** & **Explanation of Dataset** \\ \hline DIV2K & A popular dataset for SR tasks consisting of 1000 HR RGB images. It includes 800 training, 100 validation, and 100 test images. The dataset is designed for benchmarking and evaluating image SR algorithms, as well as other image restoration tasks. All images in the dataset are LR and are used as ground truth for generating lower-resolution images, which are then used to train and test SR algorithms.This dataset is essential for developing and testing new methods in image processing and computer vision due to its diverse range of high-quality images and standardized resolution. \\ \hline Set5 & A dataset composed of five HR images: baby, bird, butterfly, head, and woman.This dataset was designed for benchmarking and testing SR algorithms. It provides a small, standardized set of images to evaluate the performance of image enhancement techniques. It is commonly used for testing SR methods. This dataset is crucial for establishing benchmarks in SR research, providing a standardized set of images for consistent and comparable evaluation of different techniques. \\ \hline Set14 & Similar to Set5, Set14 includes 14 different types of HR images.The images cover a broad range of categories, including landscapes, urban scenes, and various types of textures. The images are usually available in common formats such as JPEG or PNG. The images in Set14 are HR and serve as ground truth for creating lower-resolution versions. \\ \hline BSD100 & This collection contains 100 HR photos from Berkeley’s Segmentation collection. The collection comprises natural photos with a wide range of items, textures, and situations. Researchers and developers frequently use BSD100 to assess the performance of their image processing models by running their algorithms on the dataset and comparing the results to ground truth or other models using common metrics such as the PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). \\ \hline Urban100 & Urban100, which consists of 100 HR urban scene images, is noted for its richness and complexity, covering a wide range of sizes and architectural styles. Urban100 is mostly used to evaluate the performance of image SR techniques. Researchers utilize the Urban100 dataset to assess and compare the efficacy of various SR methods. \\ \hline CelebA & A large-scale dataset of celebrity faces often used for training models that perform SR on human faces.These attributes include characteristics like gender, age, hair color, the presence of eyeglasses, facial expressions, and more. It has been used to train and evaluate models for tasks such as Attribute Prediction,Generative Models, and Face Recognition. \\ \hline Flickr2K & A high-quality image dataset containing 2650 HR images (2K resolution). It can be used for various image tasks, including SR. Researchers use it to train models and evaluate performance. \\ \hline COCO dataset & The COCO dataset contains a wide variety of images and is commonly used for tasks such as object detection, segmentation, and SR. COCO is freely available and widely used in both academic and commercial research. The dataset is continuously updated, with the most recent versions including even more annotations and categories. \\ \hline ImageNet & ImageNet is a substantial visual database used in the field of visual object recognition and classification research. It contains over 14 million annotated images across more than 20,000 categories. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been conducted using the ImageNet dataset since 2010, allowing participants to compete in tasks related to object and scene classification, as well as detection. \\ \hline \hline \end{tabular}
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Table 1: Explanation of each dataset widely used in real-time super-resolution. can be automated, although different metrics may yield inconsistent result compared to each other and subjective evaluation.