No reference PSNR estimation for compressed pictures

Turaga, Deepak S.; Yingwei, Chen; Caviedes, Jorge E.

doi:10.1016/j.image.2003.09.001

Cited by 112 publications

(58 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The first methods in this class of NR QE were designed to predict the Mean-Squared-Error (MSE) caused by block-based compression like MPEG-2 [83][84][85][86][87], JPEG [87,88], or H.264 [89,90,87,91,92]. With the exception of [84], which uses the decoded pixels v test , these techniques use information only from the received bitstream.…”

Section: Direct Estimation Of Mean-squared Errormentioning

confidence: 99%

See 1 more Smart Citation

No-reference image and video quality estimation: Applications and human-motivated design

Hemami

Reibman

2010

Signal Processing: Image Communication

122

View full text Add to dashboard Cite

Section: Direct Estimation Of Mean-squared Errormentioning

confidence: 99%

“…With the exception of [84], which uses the decoded pixels v test , these techniques use information only from the received bitstream. The basic approach is to model the DCT coefficients using a Laplacian distribution, and estimate the Laplacian parameter for each of the 8 Â 8 coefficients.…”

Section: Direct Estimation Of Mean-squared Errormentioning

confidence: 99%

No-reference image and video quality estimation: Applications and human-motivated design

Hemami

Reibman

2010

Signal Processing: Image Communication

122

View full text Add to dashboard Cite

“…Multi-scale SSIM (MS-SSIM), Gradient-based SSIM (GSSIM) and a Complex Wavelet transform domain version of SSIM (CW-SSIM) were proposed based on SSIM [4,27,28]. Also, there are other emerging issues about reduced reference [12], no-reference method [23] and hybrid perceptual bit stream method [11]. The reduced reference methods extract some features of both the original video and compressed video and compare the features to obtain a quality score.…”

Section: Video Quality Evaluation For 2d Videomentioning

confidence: 99%

An Objective Video Quality Metric for Compressed Stereoscopic Video

Seo

Liu

Kim

et al. 2011

Circuits Syst Signal Process

View full text Add to dashboard Cite

Stereoscopic video create the impression of depth by using two slightly different viewpoints. Conventional video quality assessment methods for 2D video are not suitable for stereoscopic video, so a new video quality assessment model for stereoscopic video is needed. In this paper, we propose a new objective video quality metric for compressed stereoscopic video. The proposed algorithm uses blocking artifacts, blurring in edge regions and video quality difference between two views. Blocking artifacts and blurring in edge regions are distortion appeared in the compressed video, and they are widely used in conventional video quality model. Difference in video quality between two views considers 3D effect of stereoscopic video. To verify the performance of the proposed algorithm, we performed subjective evaluation of stereoscopic video, and we confirmed that the proposed algorithm is superior to the conventional algorithms in respect to correlation with the subjective evaluation.

show abstract

“…When properly implemented, subjective methods yield accurate Objective models of image quality [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21], instead, estimate perceived quality while bypassing human assessors. These models predict image quality by processing numerical quantities (''objective features'') extracted from images.…”

Section: Introductionmentioning

confidence: 99%

“…In the specific case of picture-enhancement algorithms, one has to rely on a ''univariant'' approach, i.e., to assess the perceived quality from processed images only. Univariant approaches have recently been proposed for that purpose [10,11,[18][19][20][21], and are based on a non-linear function of image features. However, existing univariant approaches have been designed to assess the quality of compressed images rather than enhanced images.…”

Section: Introductionmentioning

confidence: 99%