Stereoscopic image quality prediction

Sazzad, Z. M. Parvez; Yamanaka, S.; Kawayokeita, Yoshikazu; Horita, Yuukou

doi:10.1109/qomex.2009.5246956

Cited by 62 publications

(27 citation statements)

References 12 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach given in [130] uses local segmented features related to degradation and dissimilarity for quality estimation of 3D images. In fact, the essential methodology used in [114] for 2D images have been extended to be employed for 3D images in [130].…”

Section: Pixel-based Features and Artifactsmentioning

confidence: 99%

See 1 more Smart Citation

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

View full text Add to dashboard Cite

The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods. Keywords:No-reference; Image quality assessment; Video quality assessment; Perceptual quality 1 Review IntroductionThere has been a tremendous progress recently in the usage of digital images and videos for an increasing number of applications. Multimedia services that have gained wide interest include digital television broadcasts, video streaming applications, and real-time audio and video services over the Internet. The global mobile data traffic grew by 81% in 2013, and during 2014, the number of mobileconnected devices will exceed the number of people on earth, according to predictions made by Cisco. The video portion of the mobile data traffic was 53% in 2013 and is expected to exceed 67% by 2018 [1]. With this huge increase in the exposure of image and video to the human eye, the interest in delivering quality of experience (QoE) may increase naturally. The quality of visual media can get degraded during capturing, compression, transmission, reproduction, and displaying due to the distortions that might occur at any of these stages. The legitimate judges of visual quality are humans as end users, the opinions of whom can be obtained by subjective experiments. Subjective experiments involve a panel of participants which are usually non-experts, also referred to as test subjects, to assess the perceptual quality of given test material such as a sequence of images or videos. Subjective experiments are typically conducted in a controlled laboratory environment. Careful planning and several factors including assessment method, selection of test material, viewing conditions, grading scale, and timing of presentation have to be considered prior to a subjective experiment. For example, Recommendation (ITU-R) BT.500 [2] provides detailed guidelines for conducting subjective experiments for the assessment of quality of television pictures. The outcomes of a subjectiv...

show abstract

Section: Pixel-based Features and Artifactsmentioning

confidence: 99%

“…In fact, the essential methodology used in [114] for 2D images have been extended to be employed for 3D images in [130]. One of the key means used to check disparity in left and right images of a stereoscopic image is the block-based edge information measure.…”

Section: Pixel-based Features and Artifactsmentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

View full text Add to dashboard Cite

show abstract

“…We evaluate our metric named Cyclop on the stereo image database used in [11]. The evaluation is performed by comparing the objective quality scores obtained by our SCIQA metric with the subjective Mean Opinion Scores (MOS) provided by [11] for the images shown in figure 3.…”

Section: Cyclop: the Proposed Metricmentioning

confidence: 99%

“…The evaluation is performed by comparing the objective quality scores obtained by our SCIQA metric with the subjective Mean Opinion Scores (MOS) provided by [11] for the images shown in figure 3. These images were JPEG coded with seven quality scales (QS: 10, 15, 27, 37, 55, 79 and 100) resulting in 637 stereoscopic symetric (same bitrate for the left and right images) and asymetric (different bitrates for the left and right images) coded image pairs in the database.…”

Section: Cyclop: the Proposed Metricmentioning

confidence: 99%

“…Their metric accounts for HVS sensitivity to contrast and luminance changes in regions of high spatial frequency. Recently, in [11], Sazzad et al proposed an objective SIQA based on the fact that perceived distortion and depth of any stereoscopic images are strongly dependent of local features such as textures and edges. Their method is intended for the evaluation of JPEG compressed images and takes into account the blockiness of the images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CYCLOP: A stereo color image quality assessment metric

Maalouf

Larabi

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Abstract-In this work, a reduced reference (RR) perceptual quality metric for color stereoscopic images is presented. Given a reference stereo pair of images and their "distorted" version, we first compute the disparity map of both the reference and the distorted stereoscopic images. To this end, we define a method for color image disparity estimation based on the structure tensors properties and eigenvalues/eigenvectors analysis. Then, we compute the cyclopean images of both the reference and the distorted pairs. Thereafter, we apply a multispectral wavelet decomposition to the two cyclopean color images in order to describe the different channels in the human visual system (HVS). Then, contrast sensitivity function (CSF) filtering is performed to obtain the same visual sensitivity information within the original and the distorted cyclopean images. Thereafter, based on the properties of the human visual system (HVS), rational sensitivity thresholding is performed to obtain the sensitivity coefficients of the cyclopean images. Finally, RR stereo color image quality assessment (SCIQA) is performed by comparing the sensitivity coefficients of the cyclopean images and studying the coherence between the disparity maps of the reference and the distorted pairs. Experiments performed on color stereoscopic images indicate that the objective scores obtained by the proposed metric agree well with the subjective assessment scores.Keywords-Stereo quality assessment, cyclopean image, 2D multi-valued wavelet, disparity field.

show abstract

Review of Existing Objective QoE Methodologies

Fang

Wang

2015

Multimedia Quality of Experience (QoE)

View full text Add to dashboard Cite

Stereoscopic image quality prediction

Cited by 62 publications

References 12 publications

No-reference image and video quality assessment: a classification and review of recent approaches

No-reference image and video quality assessment: a classification and review of recent approaches

CYCLOP: A stereo color image quality assessment metric

Review of Existing Objective QoE Methodologies

Contact Info

Product

Resources

About