40 research outputs found
No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image
Light field image quality assessment (LF-IQA) plays a significant role due to
its guidance to Light Field (LF) contents acquisition, processing and
application. The LF can be represented as 4-D signal, and its quality depends
on both angular consistency and spatial quality. However, few existing LF-IQA
methods concentrate on effects caused by angular inconsistency. Especially,
no-reference methods lack effective utilization of 2-D angular information. In
this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The
Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can
simultaneously record the angular information in both horizontal and vertical
directions. Since the MLI contains 2-D angular information, we propose a
No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI).
Specifically, we first utilize Global Entropy Distribution (GED) and Uniform
Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and
then pool them together to measure angular consistency. In addition, the
information entropy of Sub-Aperture Image (SAI) is adopted to measure spatial
quality. Extensive experimental results show that LF-QMLI achieves the
state-of-the-art performance
Deep Image Compression Using Scene Text Quality Assessment
Image compression is a fundamental technology for Internet communication
engineering. However, a high compression rate with general methods may degrade
images, resulting in unreadable texts. In this paper, we propose an image
compression method for maintaining text quality. We developed a scene text
image quality assessment model to assess text quality in compressed images. The
assessment model iteratively searches for the best-compressed image holding
high-quality text. Objective and subjective results showed that the proposed
method was superior to existing methods. Furthermore, the proposed assessment
model outperformed other deep-learning regression models.Comment: Accepted by Pattern Recognition, 202
Scalable Remote Rendering using Synthesized Image Quality Assessment
Depth-image-based rendering (DIBR) is widely used to support 3D interactive graphics on low-end mobile devices. Although it reduces the rendering cost on a mobile device, it essentially turns such a cost into depth image transmission cost or bandwidth consumption, inducing performance bottleneck to a remote rendering system. To address this problem, we design a scalable remote rendering framework based on synthesized image quality assessment. Specially, we design an efficient synthesized image quality metric based on Just Noticeable Distortion (JND), properly measuring human perceived geometric distortions in synthesized images. Based on this, we predict quality-aware reference viewpoints, with viewpoint intervals optimized by the JND-based metric. An adaptive transmission scheme is also developed to control depth image transmission based on perceived quality and network bandwidth availability. Experiment results show that our approach effectively reduces transmission frequency and network bandwidth consumption with perceived quality on mobile devices maintained. A prototype system is implemented to demonstrate the scalability of our proposed framework to multiple clients
No-reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics
We present two contributions in this work: (i) a bivariate generalized Gaussian distribution (BGGD) model for the joint distribution of luminance and disparity subband coefficients of natural stereoscopic scenes and (ii) a no-reference (NR) stereo image quality assessment algorithm based on the BGGD model. We first empirically show that a BGGD accurately models the joint distribution of luminance and disparity subband coefficients. We then show that the model parameters form good discriminatory features for NR quality assessment. Additionally, we rely on the previously established result that luminance and disparity subband coefficients of natural stereo scenes are correlated, and show that correlation also forms a good feature for NR quality assessment. These features are computed for both the left and right luminance-disparity pairs in the stereo image and consolidated into one feature vector per stereo pair. This feature set and the stereo pair׳s difference mean opinion score (DMOS) (labels) are used for supervised learning with a support vector machine (SVM). Support vector regression is used to estimate the perceptual quality of a test stereo image pair. The performance of the algorithm is evaluated over popular databases and shown to be competitive with the state-of-the-art no-reference quality assessment algorithms. Further, the strength of the proposed algorithm is demonstrated by its consistently good performance over both symmetric and asymmetric distortion types. Our algorithm is called Stereo QUality Evaluator (StereoQUE)