- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Contextual Information Based Quality Assessment for Contrast-Changed Images
摘要： In this paper, we propose the objective metric which can precisely predict the perceptual quality of contrast-changed images using inter-pixel contextual information. The metric consists of two parts. One is a 2D histogram-based contrast quality measure which utilizes the distribution of the gray-level differences between adjacent pixels. We design the desired 2D histogram considering the characteristic of an adequately high contrast image, and predict contrast quality by comparing the desired 2D histogram with 2D histograms of an original image and a contrast-changed image. The other is a spatial entropy based one which uses the information of spatial location distribution of gray-levels. A comparison is carried out with many IQA metrics on five contrast related databases. Experimental results show that the proposed metric provides a more accurate prediction of human perception of contrast change than other metrics.
关键词： 2D histogram and spatial entropy,Image quality assessment (IQA),contrast-changed images
更新于2025-09-10 09:29:36
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Stereoscopic Image Quality Assessment by Deep Convolutional Neural Network
摘要： In this paper, we propose a no-reference (NR) quality assessment method for stereoscopic images by deep convolutional neural network (DCNN). Inspired by the internal generative mechanism (IGM) in the human brain, which shows that the brain first analyzes the perceptual information and then extract effective visual information. Meanwhile, in order to simulate the inner interaction process in the human visual system (HVS) when perceiving the visual quality of stereoscopic images, we construct a two-channel DCNN to evaluate the visual quality of stereoscopic images. First, we design a Siamese Network to extract high-level semantic features of left- and right-view images for simulating the process of information extraction in the brain. Second, to imitate the information interaction process in the HVS, we combine the high-level features of left- and right-view images by convolutional operations. Finally, the information after interactive processing is used to estimate the visual quality of stereoscopic image. Experimental results show that the proposed method can estimate the visual quality of stereoscopic images accurately, which also demonstrate the effectiveness of the proposed two-channel convolutional neural network in simulating the perception mechanism in the HVS.
关键词： convolutional neural network,Image quality assessment,no reference,stereoscopic images
更新于2025-09-10 09:29:36
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PeQASO: Perceptual Quality Assessment of Streamed Videos Using Optical Flow Features
摘要： In this paper, we introduce PeQASO, a perceptual quality assessment framework for streamed videos using optical ?ow features. This approach is a reduced-reference pixel-based and relies only on the deviation of the optical ?ow of the corrupted frames. This technique compares an optical ?ow descriptor from the received frame against the descriptor obtained from the anchor frame. This approach is suitable for videos with complex motion patterns. Our technique does not make any assumptions on the coding conditions, network loss patterns or error concealment techniques. In this paper, we consider both sources of artifacts and distortions in streaming, including compression artifacts. We validate our proposed metric by testing it on a variety of distorted sequences from three proposed and commonly utilized video quality assessment databases. Our results show that our metric estimates the perceptual quality at the sequence level accurately. We report the correlation coef?cients with the differential mean opinion scores reported in the database. For compression artifacts, the results show Spearman’s and Pearson’s correlations of 0.96 and 0.94 for all the tested sequences, respectively. For channel-induced distortion, the results show Spearman’s and Pearson’s correlations of 0.88 and 0.89, respectively. For all other distortions, the average Spearman’s and Pearson’s correlations are 0.82 and 0.79, respectively.
关键词： Video quality assessment,optical ?ow,video streaming,video coding,channel losses,video quality monitoring,video distortion
更新于2025-09-09 09:28:46
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Full-Reference Image Quality Assessment by Combining Features in Spatial and Frequency Domains
摘要： Objective employs mathematical and computational theory to objectively assess the quality of output images based on the human visual system (HVS). In this paper, a novel approach based on multifeature extraction in the spatial and frequency domains is proposed. We combine the gradient magnitude and phase congruency maps to generate a local structure (LS) map, which can perceive local structural distortions. The LS matches well with HVS and highlights differences with details. For complex visual information, such as texture and contrast sensitivity, we deploy the log-Gabor filter, and spatial frequency, respectively, to effectively capture their variations. Moreover, we employ the random forest (RF) to overcome the limitations of existing pooling methods. Compared with support vector regression, RF can obtain better prediction results. Extensive experimental results on the five benchmark databases indicate that the proposed method precedes all the state-of-the-art image quality assessment metrics in terms of prediction accuracy. In addition, the proposed method is in compliance with the subjective evaluations.
关键词： log-Gabor filter,random forest (RF),contrast sensitivity function (CSF),full-reference,Image quality assessment (IQA)
更新于2025-09-09 09:28:46
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Only-Reference Video Quality Assessment for Video Coding Using Convolutional Neural Network
摘要： Conventional video quality assessment methods are either full-, reduced-, or no-reference methods that need to access decoded videos. Hence, to calculate quality of decoded video in video coding regarding an image/video quality metric, complete encoding and decoding have to executed, which is computationally expensive. To address this problem, we propose to estimate quality of decoded videos from the original video only (i.e., only-reference) using convolutional neural network, as if the original video is encoded using a range of quantization parameter. The proposed network is shallow and can be trained to estimate various video quality metrics. Furthermore, among potential rate control applications using the proposed network, we demonstrate achieving a targeted decoded-video quality by selecting a proper quantization parameter before actually encoding.
关键词： only-reference,Video quality assessment,convolutional neural network,video coding
更新于2025-09-09 09:28:46
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Blind Image Quality Assessment with Semantic Information
摘要： No-reference (NR) image quality assessment (IQA) aims to evaluate the quality of an image without reference image, which is greatly desired in the automatic visual signal processing system. Distortions degrade the visual contents and affect the semantics acquisition during the process of human perception. Although the existing methods evaluate the quality of images based on the structure, texture, or statistical characteristics, and deliver high quality prediction accuracy, they do not take the spatial semantics into account. From the perspective of human perception, distortions decrease the structural semantics that represent the structural information, and disturb the spatial semantics that describe the contents of images. Therefore, we attempt to measure the image quality by its degradation of semantics in an image. To extract the semantics of an image, a semantic network is proposed. The network contains convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) that correspond to structural semantics and spatial semantics, respectively. CNN can be regarded as a coarse imitation of human visual mechanism to obtain the structural information, and LSTM can express the contents of an image. Then, by measuring the degradations of different semantics on images, a novel NR IQA is introduced. The proposed approach is evaluated on the databases of LIVE, CSIQ, TID2013, and LIVE multiply distorted database as well as LIVE in the wild image quality challenge database, and the results show superior performance to other state-of-the-art NR IQA methods. Furthermore, we explore the generalization capability of the proposed approach, and the experimental results indicate the proposed approach has a high robustness.
关键词： spatial semantics,No-reference image quality assessment,structural semantics,human perception,semantic network
更新于2025-09-09 09:28:46
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Unified No-Reference Quality Assessment of Singly and Multiply Distorted Stereoscopic Images
摘要： A challenging problem in no-reference quality assessment of multiply distorted stereoscopic images (MDSIs) is to simulate the monocular and binocular visual properties under a mixed type of distortions. Due to the joint effects of multiple distortions in MDSIs, the underlying monocular and binocular visual mechanisms have different manifestations with those of singly distorted stereoscopic images (SDSIs). This paper presents a unified no-reference quality evaluator for SDSIs and MDSIs by learning monocular and binocular local visual primitives (MB-LVPs). The main idea is to learn MB-LVPs to characterize the local receptive field properties of the visual cortex in response to SDSIs and MDSIs. Furthermore, we also consider that the learning of primitives should be performed in a task-driven manner. For this, two penalty terms including reconstruction error and quality inconsistency are jointly minimized within a supervised dictionary learning framework, generating a set of quality-oriented MB-LVPs for each single and multiple distortion modality. Given an input stereoscopic image, feature encoding is performed using the learned MB-LVPs as codebooks, resulting in the corresponding monocular and binocular responses. Finally, responses across all the modalities are fused with probabilistic weights which are determined by the modality-specific sparse reconstruction errors, yielding the final monocular and binocular features for quality regression. The superiority of our method has been verified on several SDSI and MDSI databases.
关键词： multiply distorted,singly distorted,receptive field,monocular and binocular vision,stereoscopic image,local visual primitive,No-reference image quality assessment
更新于2025-09-09 09:28:46
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Blind Image Quality Assessment Based on Joint Log-Contrast Statistics
摘要： During recent years, quality-aware features extracted from natural scene statistics (NSS) models have been used in development of blind image quality assessment (BIQA) algorithms. Generally, the univariate distributions of bandpass coefficients are used to fit a parametric probabilistic model and the model parameters serve as the quality-aware features. However, the inter-location, inter-direction and inter-scale correlations of natural images cannot be well exploited by such NSS models, as it is hard to capture such dependencies using univariate marginal distributions. In this paper, we build a novel NSS model of joint log-contrast distribution to take into account the across space and direction correlations of natural images (inter-scale correlation to be explored as the next step). Furthermore, we provide a new efficient approach to extract quality-aware features as the gradient of log-likelihood on the NSS model, instead of using model parameters directly. Finally, we develop an effective joint-NSS model based BIQA metric called BJLC (BIQA based on joint log-contrast statistics). Extensive experiments on four public large-scale image databases have validated that objective quality scores predicted by the proposed BIQA method are in higher accordance with subjective ratings generated by human observers compared with existing methods.
关键词： partial least square,Blind image quality assessment (BIQA),no-reference (NR),natural scene statistics
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE) - Dalian, China (2018.9.20-2018.9.21)] 2018 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE) - No-Reference Quality Assessment for Stereoscopic 3D Images Based on Binocular Visual Perception
摘要： In this paper, we propose a blind/no-reference 3D image quality assessment scheme that utilizes binocular visual characteristics. The design of this scheme is motivated by studies on the perception of distorted stereoscopic images. Specifically, after the log-Gabor filter processing, the local amplitude, local phase and visual saliency are extracted from a stereopair and concatenated to form feature vectors. In addition, the binocular energy responses are also obtained as quality-predictive features. Experimental results show that the proposed scheme achieves superiority over other compared methods in terms of consistent alignment with human subjective judgments for stereoscopic images.
关键词： binocular visual perception,3D image quality assessment,no-reference
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) - San Diego, CA, USA (2018.7.23-2018.7.27)] 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) - Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information
摘要： Tone mapping operators and multi-exposure fusion methods allow us to enjoy the informative contents of high dynamic range (HDR) images with standard dynamic range devices, but also introduce distortions into HDR contents. Therefore methods are needed to evaluate tone-mapped image quality. Due to the complexity of possible distortions in a tone-mapped image, information from different scales and different levels should be considered when predicting tone-mapped image quality. So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model. After being aggregated, the extracted features are mapped to quality predictions by regression. The proposed method is tested on the largest public database for TMIQA and compared to existing no-reference methods. The experimental results show that the proposed method achieves better performance.
关键词： multi-scale and multi-layer,tone-mapped HDR images,no-reference image quality assessment
更新于2025-09-09 09:28:46