The present disclosure pertains to an image-processing device and method that make it possible to suppress block noise. A βLUT_input calculator and a clip processor determine βLUT_input, which is a value inputted to an existing-β generator and an expanded-β generator. When the value of βLUT_input qp from the clip processor is 51 or less, the existing-β generator determines β using a LUT conforming to the HVEC standard, and supplies same to a filter process determination unit. When the value of βLUT_input qp from the clip processor is larger than 51, the expanded-β generator determines the expanded β and supplies same to the filter process determination part. This disclosure, for example, can be applied to an image processing device.

Image processing device and method

We introduce a new video quality database that models video distortions in heavily-trafficked wireless networks and that contains measurements of human subjective impressions of the quality of videos. The new LIVE Mobile Video Quality Assessment (VQA) database consists of 200 distorted videos created from 10 RAW HD reference videos, obtained using a RED ONE digital cinematographic camera. While the LIVE Mobile VQA database includes distortions that have been previously studied such as compression and wireless packet-loss, it also incorporates dynamically varying distortions that change as a function of time, such as frame-freezes and temporally varying compression rates. In this article, we describe the construction of the database and detail the human study that was performed on mobile phones and tablets in order to gauge the human perception of quality on mobile devices. The subjective study portion of the database includes both the differential mean opinion scores (DMOS) computed from the ratings that the subjects provided at the end of each video clip, as well as the continuous temporal scores that the subjects recorded as they viewed the video. The study involved over 50 subjects and resulted in 5,300 summary subjective scores and time-sampled subjective traces of quality. In the behavioral portion of the article we analyze human opinion using statistical techniques, and also study a variety of models of temporal pooling that may reflect strategies that the subjects used to make the final decision on video quality. Further, we compare the quality ratings obtained from the tablet and the mobile phone studies in order to study the impact of these different display modes on quality. We also evaluate several objective image and video quality assessment (IQA/VQA) algorithms with regards to their efficacy in predicting visual quality. A detailed correlation analysis and statistical hypothesis testing is carried out. Our general conclusion is that existing VQA algorithms are not well-equipped to handle distortions that vary over time. The LIVE Mobile VQA database, along with the subject DMOS and the continuous temporal scores is being made available to researchers in the field of VQA at no cost in order to further research in the area of video quality assessment.

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Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

Considerable progress has been made toward developing still picture perceptual quality analyzers that do not require any reference picture and that are not trained on human opinion scores of distorted images. However, there do not yet exist any such completely blind video quality assessment (VQA) models. Here, we attempt to bridge this gap by developing a new VQA model called the video intrinsic integrity and distortion evaluation oracle (VIIDEO). The new model does not require the use of any additional information other than the video being quality evaluated. VIIDEO embodies models of intrinsic statistical regularities that are observed in natural vidoes, which are used to quantify disturbances introduced due to distortions. An algorithm derived from the VIIDEO model is thereby able to predict the quality of distorted videos without any external knowledge about the pristine source, anticipated distortions, or human judgments of video quality. Even with such a paucity of information, we are able to show that the VIIDEO algorithm performs much better than the legacy full reference quality measure MSE on the LIVE VQA database and delivers performance comparable with a leading human judgment trained blind VQA model. We believe that the VIIDEO algorithm is a significant step toward making real-time monitoring of completely blind video quality possible. The software release of VIIDEO can be obtained online ( http://live.ece.utexas.edu/research/quality/VIIDEO_release.zip ).

A Completely Blind Video Integrity Oracle

Quality of Experience (QoE) in multimedia applications is closely linked to the end users' perception and therefore its assessment requires subjective user studies in order to evaluate the degree of delight or annoyance as experienced by the users. QoE crowdtesting refers to QoE assessment using crowdsourcing, where anonymous test subjects conduct subjective tests remotely in their preferred environment. The advantages of QoE crowdtesting lie not only in the reduced time and costs for the tests, but also in a large and diverse panel of international, geographically distributed users in realistic user settings. However, conceptual and technical challenges emerge due to the remote test settings. Key issues arising from QoE crowdtesting include the reliability of user ratings, the influence of incentives, payment schemes and the unknown environmental context of the tests on the results. In order to counter these issues, strategies and methods need to be developed, included in the test design, and also implemented in the actual test campaign, while statistical methods are required to identify reliable user ratings and to ensure high data quality. This contribution therefore provides a collection of best practices addressing these issues based on our experience gained in a large set of conducted QoE crowdtesting studies. The focus of this article is in particular on the issue of reliability and we use video quality assessment as an example for the proposed best practices, showing that our recommended two-stage QoE crowdtesting design leads to more reliable results.

Best Practices for QoE Crowdtesting: QoE Assessment With Crowdsourcing

Databases of images or videos annotated with subjective ratings constitute essential ground truth for training, testing, and benchmarking algorithms for objective quality assessment. More than two dozen such databases are now available in the public domain; they are presented and analyzed in this paper. We propose several criteria for quantitative comparisons of source content, test conditions, and subjective ratings, which are used as the basis for the ensuing analyses and discussion. This information will allow researchers to make more well-informed decisions about databases, and may also guide the creation of additional test material and the design of future experiments.

Analysis of Public Image and Video Databases for Quality Assessment

In this paper we describe a database containing subjective assessment scores relative to 78 video streams encoded with H.264/AVC and corrupted by simulating the transmission over error-prone network. The data has been collected from 40 subjects at the premises of two academic institutions. Our goal is to provide a balanced and comprehensive database to enable reproducible research results in the field of video quality assessment. In order to support research works on Full-Reference, Reduced-Reference and No-Reference video quality assessment algorithms, both the uncompressed files and the H.264/AVC bitstreams of each video sequence have been made publicly available for the research community, together with the subjective results of the performed evaluations.

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Subjective assessment of H.264/AVC video sequences transmitted over a noisy channel

The Range Extensions (RExt) of the High Efficiency Video Coding (HEVC) standard have recently been approved by both ITU-T and ISO/IEC. This set of extensions targets video coding applications in areas including content acquisition, postproduction, contribution, distribution, archiving, medical imaging, still imaging, and screen content. In addition to the functionality of HEVC Version 1, RExt provide support for monochrome, 4:2:2, and 4:4:4 chroma sampling formats as well as increased sample bit depths beyond 10 bits per sample. This extended functionality includes new coding tools with a view to provide additional coding efficiency, greater flexibility, and throughput at high bit depths/rates. Improved lossless, near-lossless, and very high bit-rate coding is also a part of the RExt scope. This paper presents the technical aspects of HEVC RExt, including a discussion of RExt profiles, tools, applications, and provides experimental results for a performance comparison with previous relevant coding technology. When compared with the High 4:4:4 Predictive Profile of H.264/Advanced Video Coding (AVC), the corresponding HEVC 4:4:4 RExt profile provides up to $\sim 25$ %, $\sim 32$ %, and $\sim 36$ % average bit-rate reduction at the same PSNR quality level for intra, random access, and low delay configurations, respectively.

Overview of the Range Extensions for the HEVC Standard: Tools, Profiles, and Performance

This paper describes a publicly available database of subjective scores, relative to quality assessment of 156 video streams encoded with H.264/AVC and corrupted by simulating packet losses over an error-prone network. The data has been collected in controlled test environments at the premises of two academic institutions. A detailed statistical analysis of subjective results has been performed, showing high consistency of the collected scores. In addition to subjective scores, we have made available to the research community both the uncompressed files and the H.264/AVC bitstreams of each video sequence, in order to provide a common database of benchmark data to test and compare the performance of full-reference, reduced-reference and no-reference video quality assessment algorithms.

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A H.264/AVC video database for the evaluation of quality metrics

In this paper we propose a robust approach to detect moving objects for video surveillance applications. We demonstrate that a jointly use of frame by frame difference with a background subtraction algorithm allows us to have a strong and fast pixel foreground classification without the need of previous background learning. The Joint Difference algorithm uses frame difference information to correct pixels classification made by a background subtraction algorithm while selectively updating the background model according to such classification. In this way we should perform motion segmentation also in presence of environmental changes such as illumination variations or "waking persons". The algorithm is capable of 15 fps tracking of moving people on 640-480 unsampled color images; results on both VSSN06 and Wallflower [8] benchmark videos are presented.

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A revaluation of frame difference in fast and robust motion detection

The under development High Efficiency Video Coding (HEVC) standard employs several powerful coding tools to obtain improved compression efficiency regarding the state-of-the-art H.264/AVC standard. To efficiently exploit the temporal and spatial redundancies, HEVC adopts a very flexible quadtree coding structure, allowing the encoder to use a block partition better matching the image features. The best combination of HEVC block partitioning and coding modes is found by means of a rate distortion optimization process. During this minimization, the encoder tests all the possible coding modes and block partitions and keeps those providing the smallest RD cost. Due to the large number of available modes and partitions, the minimization involves extremely high complexity, which may not be suitable for real-time application scenarios. To reduce the complexity, two novel fast RDO techniques are proposed in this paper: Top Skip and Early Termination. The experimental evaluation reveals that the proposed techniques significantly reduce the encoding time, notably up to 45% regarding the high complexity HEVC reference software codec, with a negligible quality loss never larger than 0.1 dB.

Matteo Naccari

Papers

Subjective assessment of H.264/AVC video sequences transmitted over a noisy channel

Overview of the Range Extensions for the HEVC Standard: Tools, Profiles, and Performance

A H.264/AVC video database for the evaluation of quality metrics

A revaluation of frame difference in fast and robust motion detection

Fast rate distortion optimization for the emerging HEVC standard