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The importance of visual quality in point clouds has been significantly underlined due to the rapid rise in 3D vision applications which aim to deliver affordable and superior user experiences. Reviewing the evolution of point cloud quality assessment (...

This paper introduces a novel approach to Image Quality Assessment (IQA) by presenting a new loss function, Dual-Criterion Quality (DCQ) Loss, which integrates the Mean Squared Error (MSE) framework with a Relative Perception Constraint (RPC). The RPC is ...

No-reference image quality assessment (NR-IQA) methods, inspired by the free energy principle, improve the accuracy of image quality prediction by simulating the human brain’s repair process for distorted images. However, existing methods use separate ...

Nowadays, most three-dimensional model quality assessment (3DQA) methods have been aimed at improving accuracy. However, little attention has been paid to the computational cost and inference time required for practical applications. Model-based 3DQA ...

With the ever-growing demand for high-quality video content, methods for evaluating the aesthetic quality of digital videos have become invaluable tools to ensure a satisfying viewing experience for the end-user. Over the years, many accurate and ...

Image quality assessment (IQA) is an important problem in computer vision with many applications. We propose a transformer-based multi-task learning framework for the IQA task. Two subtasks: constructing an auxiliary information error map and completing ...

Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc. In practice, the quality of CGIs consistently suffers from poor rendering ...

No-reference image quality assessment is a basic and challenging problem in the field of image processing. Among them, contrast distortion has a great impact on the perception of image quality. However, there are relatively few studies on no-reference ...

The quality of speech degrades while communicating over Voice over Internet Protocol applications, for example, Google Meet, Microsoft Skype, and Apple FaceTime, due to different types of background noise present in the surroundings. It reduces human ...

In panoramic multimedia applications, the perception quality of the omnidirectional content often comes from the observer's perception of the viewports and the overall impression after browsing. Starting from this hypothesis, this paper proposes a deep-...

Generative adversarial networks (GANs) have been extensively used for training networks that perform image generation. After training, the discriminator in GAN was not used anymore. We propose to recycle the trained discriminator for another use: no-...

Blind assessment of video quality is still challenging even in this deep learning era. The limited number of samples in existing databases is insufficient to learn a good feature extractor for video quality assessment (VQA), while manually labeling a ...

We propose an end-to-end saliency-guided deep neural network (SGDNet) for no-reference image quality assessment (NR-IQA). Our SGDNet is built on an end-to-end multi-task learning framework in which two sub-tasks including visual saliency prediction and ...

In recent years, the research of the no-reference (NR) image quality assessment (IQA) has been very active. But the NR-IQA research for noise-distorted images hasn't been proposed yet. In this paper, we propose a NR-IQA metric for noise-distorted images ...

The proposed metric is to use latent quality aware topics in an image to measure blurriness. A novel image quality vocabulary is firstly obtained by the contrast features computed from the training images using K-means. Probabilistic latent semantic ...

In this paper, we propose a novel no reference image quality assessment method. This method performs image quality assessment by incorporating a graphical model. To obtain the results of the image quality assessment, first, we use a set of pristine and ...

Methods to undo the effects of motion blur are the subject of intense research, but evaluating and tuning these algorithms has traditionally required either user input or the availability of ground-truth images. We instead develop a metric for ...

Non-local means provides a very powerful framework to denoise digital images. Nevertheless, there are several influential parameters on this methodology that are data-dependent and difficult to tune. This paper presents an adaptive image denoising ...

In this paper, we present a novel no-reference blur metric for images. The blur metric is based on analyzing image features include the mean value of phase congruency image, the entropy of phase congruency image and the distorted image, and the gradient ...

No-Reference (NR) quality metric for monitoring video image is a challenging and meaningful research. In this paper, we propose a novel objective NR video blurriness metric for monitoring surveillance tapes based on human visual system (HVS) ...