Subjective and objective quality evaluation of synthetic and high dynamic range images

Recent years have seen a huge growth in the acquisition, transmission, and storage of videos. The visual data consists of both natural scenes as well as synthetic scenes, such as animated movies, cartoons and video games. In all these cases, the ultimate goal is to provide the viewers with a satisfactory quality-of-experience. In addition to the traditional 8-bit images, high dynamic range imaging is also becoming popular because of its ability to represent the real world luminances more realistically. Coming up with objective image quality assessment algorithms for these applications is an interesting research problem. In this work, I have developed a synthetic image quality database by introducing varying degrees of different types of distortions and conducted a subjective experiment in order to obtain the ground-truth data. I evaluated the performance of state-of-the-art image quality assessment algorithms (typically meant for natural images) on this database, especially no-reference algorithms that have not been applied to the domain of computer graphics images before. I identified the top-performing algorithms along with analyzing the types of distortions on which the present algorithms show a less impressive performance. For high dynamic range(HDR) images, I have designed two new full-reference image quality assessment algorithms to judge the quality of tonemapped HDR images using statistical features extracted from them. I have also conducted a massive online crowd-sourced subjective test for HDR image artifacts arising from tonemapping, multiple-exposure fusion and post processing. To the best of our knowledge, presently this is the largest HDR image database in the world involving the largest number of source images and most number of human evaluations. Based on the subjective evaluations obtained, I have also proposed machine learning based no-reference image quality assessment algorithms to predict the perceptual quality of HDR images.Electrical and Computer Engineerin

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Automated mining of the ALMA archive in the COSMOS field (A3COSMOS): I. Robust ALMA continuum photometry catalogs and stellar mass and star formation properties for ∼700 galaxies at z = 0.5–6

The rich information on (sub)millimeter dust continuum emission from distant galaxies in the public Atacama Large Millimeter/submillimeter Array (ALMA) archive is contained in thousands of inhomogeneous observations from individual PI-led programs. To increase the usability of these data for studies deepening our understanding of galaxy evolution, we have developed automated mining pipelines for the ALMA archive in the COSMOS field (A3COSMOS) which efficiently exploit the available information for large numbers of galaxies across cosmic time, and keep the data products in sync with the increasing public ALMA archive: (a) a dedicated ALMA continuum imaging pipeline; (b) two complementary photometry pipelines for both blind source extraction and prior source fitting; (c) a counterpart association pipeline utilizing the multi-wavelength data available (including quality assessment based on machine-learning techniques); (d) an assessment of potential (sub-)mm line contribution to the measured ALMA continuum; and (e) extensive simulations to provide statistical corrections to biases and uncertainties in the ALMA continuum measurements. Application of these tools yields photometry catalogs with ∼ 1000 (sub-)mm detections (spurious fraction ∼ 8 − 12%) from over 1500 individual ALMA continuum images. Combined with ancillary photometric and redshift catalogs and the above quality assessments, we provide robust information on redshift, stellar mass and star formation rate for ∼700 galaxies at redshifts 0.5-6 in the COSMOS field (with undetermined selection function). The ALMA photometric measurements and galaxy properties are released publicly within our blind-extraction, prior-fitting and galaxy property catalogs, plus the images. These products will be updated on a regular basis in the future

Anomaly Detection in Autonomous Driving: A Survey

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.Comment: Daniel Bogdoll and Maximilian Nitsche contributed equally. Accepted for publication at CVPR 2022 WAD worksho

LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space

Light field imaging can capture both the intensity information and the direction information of light rays. It naturally enables a six-degrees-of-freedom viewing experience and deep user engagement in virtual reality. Compared to 2D image assessment, light field image quality assessment (LFIQA) needs to consider not only the image quality in the spatial domain but also the quality consistency in the angular domain. However, there is a lack of metrics to effectively reflect the angular consistency and thus the angular quality of a light field image (LFI). Furthermore, the existing LFIQA metrics suffer from high computational costs due to the excessive data volume of LFIs. In this paper, we propose a novel concept of "anglewise attention" by introducing a multihead self-attention mechanism to the angular domain of an LFI. This mechanism better reflects the LFI quality. In particular, we propose three new attention kernels, including anglewise self-attention, anglewise grid attention, and anglewise central attention. These attention kernels can realize angular self-attention, extract multiangled features globally or selectively, and reduce the computational cost of feature extraction. By effectively incorporating the proposed kernels, we further propose our light field attentional convolutional neural network (LFACon) as an LFIQA metric. Our experimental results show that the proposed LFACon metric significantly outperforms the state-of-the-art LFIQA metrics. For the majority of distortion types, LFACon attains the best performance with lower complexity and less computational time.Comment: Accepted for IEEE VR 2023 (TVCG Special Issues) (Early Access