No-Reference Image Quality Assessment

The CSIQ database consists of 30 original images, each is distorted using six different types of distortions at four to five different levels of distortion. CSIQ images are subjectively rated base on a linear displacement of the images across four calibrated LCD monitors placed side by side with equal viewing distance to the observer. The database contains 5000 subjective ratings from 35 different observers, and ratings are reported in the form of DMOS.

KonIQ-10k is a large-scale IQA dataset consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models.

The Smartphone Photography Attribute and Quality (SPAQ) dataset is a comprehensive database for the perceptual quality assessment of smartphone photography. It was introduced in a paper titled "Perceptual Quality Assessment of Smartphone Photography" presented at the IEEE Conference on Computer Vision and Pattern Recognition in 2020. The SPAQ dataset consists of 11,125 pictures taken by 66 smartphones. Each image in the dataset is attached with rich annotations, including: - Image quality - Image attributes (brightness, colorfulness, contrast, noisiness, and sharpness) - Scene category labels (animal, cityscape, human, indoor scene, landscape, night scene, plant, still life, and others) - Exchangeable image file format (EXIF) data These annotations were collected in a well-controlled laboratory environment. The dataset has been used to train blind image quality assessment (BIQA) models, providing insights into how EXIF data, image attributes, and high-level semantics interact with image quality. The SPAQ dataset and the proposed BIQA models are available on GitHub.

Konstanz artificially distorted image quality database (KADID-10k) contains 81 pristine images, each degraded by 25 distortions in 5 levels.

The dataset was created for video quality assessment problem. It was formed with 36 clips from Vimeo, which were selected from 18,000+ open-source clips with high bitrate (license CCBY or CC0). The clips include videos recorded by both professionals and amateurs. Almost half of the videos contain scene changes and high dynamism. Moreover, the synthetic to natural lightning ratio is approximately 1 to 3. * Content type: nature, sport, humans close up, gameplays, music videos, water stream or steam, CGI * Effects and distortions: shaking, slow-motion, grain/noisy, too dark/bright regions, macro shooting, captions (text), extraneous objects on the camera lens or just close to it * Resolution: 1920x1080 as the most popular modern video resolution (more in the future) * Format: yuv420p * FPS: 24, 25, 30, 39, 50, 60 * Videos duration: mainly 10 seconds Such content diversity helps simulate near-realistic conditions. The choice of videos collected for the benchmark dataset employed clustering in terms of space-time complexity to obtain a representative distribution. For compression we used 40 codecs of 10 compression standards (H.264, AV1, H.265, VVC, etc.). Each video was compressed with 3 target bitrates: 1,000 Kbps, 2,000 Kbps, and 4,000 Kbps, and different real-life encoding modes: constant quality (CRF) and variable bitrate (VBR). The choice of bitrate range simplifies the subjective comparison procedure since the video quality is more difficult to distinguish visually at higher bitrates. The subjective assessment involved pairwise comparisons using crowdsourcing service Subjectify.us. To increase the relevance of the results, each pair of videos received at least 10 responses from participants. In total, 766362 valid answers were collected from more than 10800 unique participants.

TID2013 is a dataset for image quality assessment that contains 25 reference images and 3000 distorted images (25 reference images x 24 types of distortions x 5 levels of distortions). Source: Lukin et al

We introduce a novel Image Quality Assessment (IQA) dataset comprising 6073 UHD-1 (4K) images, annotated at a fixed width of 3840 pixels. Contrary to existing No-Reference (NR) IQA datasets, ours focuses on highly aesthetic photos of high technical quality, filling a gap in the literature. The images, carefully curated to exclude synthetic content, are sufficiently diverse to train general NR-IQA models. Importantly, the dataset is annotated with perceptual quality ratings obtained through a crowdsourcing study. Ten expert raters, comprising photographers and graphics artists, assessed each image at least twice in multiple sessions spanning several days, resulting in highly reliable labels. Annotators were rigorously selected based on several metrics, including self-consistency, to ensure their reliability. The dataset includes rich metadata with user and machine-generated tags from over 5,000 categories and popularity indicators such as favorites, likes, downloads, and views. With its unique characteristics, such as its focus on high-quality images, reliable crowdsourced annotations, and high annotation resolution, our dataset opens up new opportunities for advancing perceptual image quality assessment research and developing practical NRIQA models that apply to modern photos.

Briefly describe the dataset. Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset If the description or image is from a different paper, please refer to it as follows: Source: title Image Source: title

Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA). Due to its subjective nature, it is necessary to estimate and guarantee the consistency of the IQA process, a characteristic lacking in the mean opinion scores (MOS) widely used for crowdsourcing IQA. In addition, existing blind IQA (BIQA) datasets pay little attention to the difficulty of cross-content assessment, which may degrade the quality of annotations. This paper introduces PIQ23, a portrait-specific IQA dataset of 5116 images of 50 predefined scenarios acquired by 100 smartphones, covering a high variety of brands, models, and use cases. The dataset includes individuals of various genders and ethnicities who have given explicit and informed consent for their photographs to be used in public research. It is annotated by pairwise comparisons (PWC) collected from over 30 image quality experts for three image attributes: face detail preservation, face target exposure, and overall image quality. An in-depth statistical analysis of these annotations allows us to evaluate their consistency over PIQ23. Finally, we show through an extensive comparison with existing baselines that semantic information (image context) can be used to improve IQA predictions. The dataset along with the proposed statistical analysis and BIQA algorithms are available: https://github.com/DXOMARKResearch/PIQ2023