Referenceless Measure of Blocking Artifacts by Tchebichef Kernel Analysis

Li, Leida; Zhu, Hancheng; Yang, Gaobo; Qian, Jiansheng

doi:10.1109/lsp.2013.2294333

Cited by 93 publications

(21 citation statements)

References 11 publications

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“…Pearson linear correlation coefficient (PLCC) and root mean square error (RMSE) were used to evaluate the prediction accuracy, and Spearman rank-order correlation coefficient (SROCC) was used to evaluate the prediction monotonicity. These criterions were computed between the predicted scores and the subjective scores, where nonlinear fitting was first conducted to bring them on the same scale [10][21]. For comparison, the results of some popular no-reference blocking artifact metrics were also provided, including Perra’s [2], Wang’s [6], Pan’s [7], Lee’s [8], Li’s [10], Bovik’s [22], Wang’s [23], Liu’s [24], Chen’s [25], Mittal’s [26], Ye’s [27], Liu’s [28], and Liu’s [29].…”

Section: Resultsmentioning

confidence: 99%

“…Li et al [9] presented a blockiness metric which computes the regularities of pseudo structures. In [10], Li et al proposed a quality measure for JPEG-compressed images based on Tchebichef kernels. Golestaneh and Chandler [11] presented a measure that gets the number of zero-valued discrete cosine transform (DCT) coefficients within each block.…”

Section: Literature Reviewmentioning

confidence: 99%

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A No-Reference Adaptive Blockiness Measure for JPEG Compressed Images

Tang

Wang

2016

PLoS ONE

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Digital images have been extensively used in education, research, and entertainment. Many of these images, taken by consumer cameras, are compressed by the JPEG algorithm for effective storage and transmission. Blocking artifact is a well-known problem caused by this algorithm. Effective measurement of blocking artifacts plays an important role in the design, optimization, and evaluation of image compression algorithms. In this paper, we propose a no-reference objective blockiness measure, which is adaptive to high frequency component in an image. Difference of entropies across blocks and variation of block boundary pixel values in edge images are adopted to calculate the blockiness level in areas with low and high frequency component, respectively. Extensive experimental results prove that the proposed measure is effective and stable across a wide variety of images. It is robust to image noise and can be used for real-world image quality monitoring and control. Index Terms—JPEG, no-reference, blockiness measure

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A No-Reference Adaptive Blockiness Measure for JPEG Compressed Images

Tang

Wang

2016

PLoS ONE

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“…A popular approach consists of estimating image quality using distortion-specific (DS) methods that measure the intensity of the most relevant image distortions. Among the state-of-the-art DS methods, we can cite the papers of Fang et al [6], Bahrami and Kot [7], Golestaneh and Chandler [8], and Li et al [9][10][11]. These methods make assumptions about the type of distortion present in the signal and, as a consequence, have limited applications in more diverse multimedia scenarios.…”

Section: Introductionmentioning

confidence: 99%

Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

Freitas

Akamine

2018

J Braz Comput Soc

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In most practical multimedia applications, processes are used to manipulate the image content. These processes include compression, transmission, or restoration techniques, which often create distortions that may be visible to human subjects. The design of algorithms that can estimate the visual similarity between a distorted image and its non-distorted version, as perceived by a human viewer, can lead to significant improvements in these processes. Therefore, over the last decades, researchers have been developing quality metrics (i.e., algorithms) that estimate the quality of images in multimedia applications. These metrics can make use of either the full pristine content (full-reference metrics) or only of the distorted image (referenceless metric). This paper introduces a novel referenceless image quality assessment (RIQA) metric, which provides significant improvements when compared to other state-of-the-art methods. The proposed method combines statistics of the opposite color local variance pattern (OC-LVP) descriptor with statistics of the opposite color local salient pattern (OC-LSP) descriptor. Both OC-LVP and OC-LSP descriptors, which are proposed in this paper, are extensions of the opposite color local binary pattern (OC-LBP) operator. Statistics of these operators generate features that are mapped into subjective quality scores using a machine-learning approach. Specifically, to fit a predictive model, features are used as input to a gradient boosting machine (GBM). Results show that the proposed method is robust and accurate, outperforming other state-of-the-art RIQA methods.

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“…The early NR IQA algorithms mainly focus on the DS metrics, and they assume the distortion type is known in advance, such as JPEG compression distortion [12], contrast distortion [13], blocking artifacts [14] and blur distortion [15]. As the environment of real-world is complex and dynamic, images usually suffer from multiple distortions.…”

Section: Introductionmentioning

confidence: 99%