Subjective and objective quality evaluation of lar coded art images

Strauss, Clément; Pasteau, François; Autrusseau, Florent; Babel, Marie; Bédat, Laurent; Déforges, Olivier

doi:10.1109/icme.2009.5202586

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…Self-extraction of regions of interest is also possible [4]. Furthermore, subjective quality image evaluation shows that the low resolution LAR picture is better than the same bit-rate JPEG2000 coded picture [5].…”

Section: Lar Modelingmentioning

confidence: 99%

Quality constraint and rate-distortion optimization for predictive image coders

2013

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International audienceNext generations of image and video coding methods should of course be efficient in terms of compression, but also propose advanced functionalities. Among these functionalities such as scalability, lossy and lossless coding, data protection, Rate Distortion Optimization (RDO) and Rate Control (RC) are key issues. RDO aims at optimizing compression performances, while RC mechanism enables to exactly compress at a given rate. A less common functionality than RC, but certainly more helpful, is Quality Control (QC): the constraint is here given by the quality. In this paper, we introduce a joint solution for RDO and QC applied to a still image codec called Locally Adaptive Resolution (LAR), providing scalability both in resolution and SNR and based on a multi-resolution structure. The technique does not require any additional encoding pass. It relies on a modeling and estimation of the prediction errors obtained in an early work. First, quality constraint is applied and propagated through the whole resolution levels called pyramid. Then, the quantization parameters are deduced considering inter and intra pyramid level relationships. Results show that performances of the proposed method are very close to an exhaustive search solution

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Section: Lar Modelingmentioning

confidence: 99%

Quality constraint and rate-distortion optimization for predictive image coders

2013

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“…As can be seen from Table 1, experimental data are generated from original images . Various distortions are added to these images at different levels to form testing images whose quality is assessed via subjective rating by a certain number of observers (usually from [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The testing methods frequently used are "Double stimulus categorical rating" (DCIS) and "Single stimulus categorical rating" (e.g., Absolute Category Rating (ACR).…”

Section: Related Work a Iqa Databasesmentioning

confidence: 99%

“…All existing databases evaluate subjective quality for full image while the quality in its regions is far different. Because [11] 2010 Full DMOS+σ 866 30 5 6 25 5-7 512×512 Custom IVC(I) [12,20] 2005 Full DMOS+σ 185 10 5 3 15 15 512×512 DSIS 65% IVC-3D [21] 2008 Full DMOS 90 6 5 3 19 19 512×512 SAMVIQ IVC-Art [22,23] 2009 Full Raw 120 8 5 3 19 19 512×512 DSIS LIVE(I) [8,17] 2006 Full DMOS+σ 779 29 7-8 5 20-29 768×512 ACR 88% MICT [13] 2008 Full Raw 196 14 5 2 16 16 768×512 ACR 61% MMSP-3D(I) [24] 2010 Full MOS+σ 54 9 4 1 27 27 768×512 ACR 62% TID2008 [9,25] 2008 of compressing or adding noise to the image, each region has its own characteristics resulting in annoying artifacts with which HVS has different sensitivity. Moreover, there is a minimum visibility threshold which no change can be perceived below [26].…”

Section: Related Work a Iqa Databasesmentioning

confidence: 99%

End-to-End Image Patch Quality Assessment for Image/Video With Compression Artifacts

Pham¹,

Hoang

Nguyen³

et al. 2020

IEEE Access

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In this paper, we present an experimental image quality assessment (IQA) method for image/ video patches with compression artifacts. Using the High Efficiency Video Coding (HEVC) standard, we create a new database of image patches with compression artifacts. Then, we conduct a completed subjective testing process to obtain the 'ground truth' quality scores for the mentioned database. Finally, we employ an end-to-end learning method to estimate the IQA model for the patches with HEVC compression artifacts. In such proposed method, a modified convolutional neural network (CNN) architecture is exploited for feature extraction while an adaptive moment estimation optimizer solution is used to perform the training process. Experimental results show that the proposed end-to-end IQA method significantly outperforms the relevant IQA benchmarks, especially when the compression artifacts are strongly realized in image/video patches. The proposed IQA method is expected to drive a new set of image/video compression solutions in future image/video coding and transmissions.

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“…The VLIC dataset consists of 50 reference scenes coming from previous image compression and image quality studies. The stimuli are taken from the Rawzor's free dataset (14 images) 2 , CSIQ dataset (30 images) [9] and the subjective quality dataset in [10] (where we randomly selected 6 images from the 10 images in the dataset). For Rawzor's dataset, images were cropped to 960x600 pixels to fit on our screen.…”

Section: A Stimulimentioning

confidence: 99%

Visibility Metric for Visually Lossless Image Compression

Pérez-Ortiz

Mantiuk

2019

2019 Picture Coding Symposium (PCS)

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Encoding images in a visually lossless manner helps to achieve the best trade-off between image compression performance and quality and so that compression artifacts are invisible to the majority of users. Visually lossless encoding can often be achieved by manually adjusting compression quality parameters of existing lossy compression methods, such as JPEG or WebP. But the required compression quality parameter can also be determined automatically using visibility metrics. However, creating an accurate visibility metric is challenging because of the complexity of the human visual system and the effort needed to collect the required data. In this paper, we investigate how to train an accurate visibility metric for visually lossless compression from a relatively small dataset. Our experiments show that prediction error can be reduced by 40% compared with the state-of-theart, and that our proposed method can save between 25%-75% of storage space compared with the default quality parameter used in commercial software. We demonstrate how the visibility metric can be used for visually lossless image compression and for benchmarking image compression encoders.

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Subjective and objective quality evaluation of lar coded art images

Cited by 11 publications

References 7 publications

Quality constraint and rate-distortion optimization for predictive image coders

Quality constraint and rate-distortion optimization for predictive image coders

End-to-End Image Patch Quality Assessment for Image/Video With Compression Artifacts

Visibility Metric for Visually Lossless Image Compression

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