Compressed image quality assessment plays an important role in image services, especially in image compression applications, which can be utilized as a guidance to optimize image processing algorithms. In this paper, we propose an objective image quality assessment algorithm to measure the quality of compressed images. The proposed method utilizes a data-driven transform, Saak (Subspace approximation with augmented kernels), to decompose images into hierarchical structural feature space. We measure the distortions of Saak features and accumulate these distortions according to the feature importance to human visual system. Compared with the state-of-the-art image quality assessment methods on widely utilized datasets, the proposed method correlates better with the subjective results. In addition, the proposed methods achieves more robust results on different datasets.
University of Science and Technology of China（中国科学技术大学）
Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment (IQA) for compressed images can not only help to verify the performance of various compression algorithms but also help to guide the compression optimization in turn. In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space. It is known that the compression artifacts are usually non-uniformly distributed w…
In the video coding process, the perceived quality of a compressed video is evaluated by full-reference quality evaluation metrics. However, it is difficult to obtain reference videos with perfect quality. To solve this problem, it is critical to design no-reference compressed video quality assessment algorithms, which assists in measuring the quality of experience on the server side and resource allocation on the network side. Convolutional Neural Network (CNN) has shown its advantage in Video Quality Assessment (VQA) with promising successes in recent years. A large-scale quality database is…
In this paper, we propose a deep learning based video quality assessment (VQA) framework to evaluate the quality of the compressed user's generated content (UGC) videos. The proposed VQA framework consists of three modules, the feature extraction module, the quality regression module, and the quality pooling module. For the feature extraction module, we fuse the features from intermediate layers of the convolutional neural network (CNN) network into final quality-aware feature representation, which enables the model to make full use of visual information from low-level to high-level. Specifica…
RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation
School of Electronics and Information, Northwestern Polytechnical University（电子与信息学院，西北工业大学）；School of Engineering, Royal Melbourne Institute of Technology（工程学院，皇家墨尔本理工学院）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）；School of Telecommunication Engineering, Xidian University（电信工程学院，西安电子科技大学）
High frame rates have been known to enhance the perceived visual quality of specific video content. However, the lack of investigation of high frame rates has restricted the expansion of this research field particularly in the context of full-high-definition (FHD) and 4K ultra-high-definition video formats. This study involves a subjective and objective quality assessment of compressed FHD videos. First, we compress the FHD videos by employing high-efficiency video coding, and VP9 at five quantization parameter levels for multiple frame rates, i.e., 15fps, 30fps, and 60fps. The FHD videos are …
Over the past decade, the online video industry has greatly expanded the volume of visual data that is streamed and shared over the Internet. Moreover, because of the increasing ease of video capture, many millions of consumers create and upload large volumes of User-Generated-Content (UGC) videos. Unlike streaming television or cinematic content produced by professional videographers and cinemagraphers, UGC videos are most commonly captured by naive users having limited skills and imperfect technique, and often are afflicted by highly diverse and mixed in-capture distortions. These UGC videos…
Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information. In addition to other challenges in immersive applications, objective and subjective quality assessments of compressed 3D content remain open problems and an area of research interest. Yet most of the efforts in the research area ignore the local geometrical structures between points representation. In this paper, we overcome this limitation by introducing a novel…
One major problem of objective Image Quality Assessment (IQA) methods is the lack of linearity of their quality estimates with respect to scores expressed by human subjects. For this reason, usually IQA metrics undergo a calibration process based on subjective quality examples. However, example-based training makes generalization problematic, hampering result comparison across different applications and operative conditions. In this paper, new Full Reference (FR) techniques, providing estimates linearly correlated with human scores without using calibration are introduced. To reach this object…
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding
University of Konstanz（康斯坦茨大学）；Cloudinary（Cloudinary公司）；IST-IT（IST-IT机构）；Fraunhofer IIS（弗劳恩霍夫应用固体物理研究所）；Huawei（华为公司）；Takushoku University（拓殖大学）；IT-UBI（IT-UBI机构）；EPFL（洛桑联邦理工学院）
Lomonosov Moscow State University（莫斯科罗蒙诺索夫国立大学）；Dubna State University（杜布纳国立大学）
Subjective image-quality measurement plays a critical role in the development of image-processing applications. The purpose of a visual-quality metric is to approximate the results of subjective assessment. In this regard, more and more metrics are under development, but little research has considered their limitations. This paper addresses that deficiency: we show how image preprocessing before compression can artificially increase the quality scores provided by the popular metrics DISTS, LPIPS, HaarPSI, and VIF as well as how these scores are inconsistent with subjective-quality scores. We p…
