Detection of blocking artifacts in compressed video

Vlachos, T.

doi:10.1049/el:20000847

Cited by 74 publications

(41 citation statements)

References 3 publications

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“…Ironically, recent developments in the field of video quality metrics are less likely to be suitable for robotic perception as their metrics are strongly tailored to the HVS or specific artefacts due to transmission or compression. For example, metrics designed to react to phenomena such as blocking [5,6,7] and ringing [8] are not particularly relevant to robotic perception. Similarly, highlevel metrics modelled on physical aspects of the HVS [3,4] were excluded.…”

Section: Related Workmentioning

confidence: 99%

Visual metrics for the evaluation of sensor data quality in outdoor perception

Brunner

Peynot

2010

Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop

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Abstract-This paper proposes an experimental study of quality metrics that can be applied to visual and infrared images acquired from cameras onboard an unmanned ground vehicle (UGV). The relevance of existing metrics in this context is discussed and a novel metric is introduced. Selected metrics are evaluated on data collected by a UGV in clear and challenging environmental conditions, represented in this paper by the presence of airborne dust or smoke. An example of application is given with monocular SLAM estimating the pose of the UGV while smoke is present in the environment. It is shown that the proposed novel quality metric can be used to anticipate situations where the quality of the pose estimate will be significantly degraded due to the input image data. This leads to decisions of advantageously switching between data sources (e.g. using infrared images instead of visual images).

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Section: Related Workmentioning

confidence: 99%

Visual metrics for the evaluation of sensor data quality in outdoor perception

Brunner

Peynot

2010

Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop

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“…• The blockiness metric is a modification of the metric by Vlachos (Vlachos, 2000). It estimates the blockiness signal strength by comparing the cross-correlation of pixels inside (intra) and outside (inter) the borders of the coding blocking structure of a frame.…”

Section: No-reference Video Quality Metric Based On Artifact Measuremmentioning

confidence: 99%

Video Quality Metrics

Farias¹

2010

Digital Video

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“…As a consequence, metrics following the NR approach such as [11,24,36] usually provide inferior quality prediction performance as compared to metrics that take into account some amount of reference information from the transmitted image, or process the whole original image itself as in case of FR metrics. Furthermore, as NR metrics provide an absolute measure about the quality of a received image, it may be difficult to distinguish quality degradations that have been induced during image transmission from those that have already been present in the image prior to transmission.…”

Section: Visual Quality Assessmentmentioning

confidence: 99%

Reduced-reference metric design for objective perceptual quality assessment in wireless imaging

Engelke

Kusuma

Zepernick

et al. 2009

Signal Processing: Image Communication

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The rapid growth of third and development of future generation mobile systems has led to an increase in the demand for image and video services. However, the hostile nature of the wireless channel makes the deployment of such services much more challenging, as in the case of a wireline system. In this context, the importance of taking care of user satisfaction with service provisioning as a whole has been recognized. The related user-oriented quality concepts cover end-to-end quality of service and subjective factors such as experiences with the service. To monitor quality and adapt system resources, performance indicators that represent service integrity have to be selected and related to objective measures that correlate well with the quality as perceived by humans. Such objective perceptual quality metrics can then be utilized to optimize quality perception associated with applications in technical systems.In this paper, we focus on the design of reduced-reference objective perceptual image quality metrics for use in wireless imaging. Specifically, the Normalized Hybrid Image Quality Metric (NHIQM) and a perceptual relevance weighted -norm are designed. The main idea behind both feature-based metrics relates to the fact that the human visual system (HVS) is trained to extract structural information from the viewing area. Accordingly, NHIQM and -norm are designed to account for different structural artifacts that have been observed in our distortion model of a wireless link. The extent by which individual artifacts are present in a given image is obtained by measuring related image features. The overall quality measure is then computed as a weighting sum of the features with the respective perceptual relevance weight obtained from subjective experiments. The proposed metrics differ mainly in the pooling of the features and amount of reduced-reference produced. While NHIQM performs the pooling at the transmitter of the system to produce a single value as reduced-reference, the -norm requires all involved feature values from the transmitted and received image to perform the pooling on the feature differences at the receiver. In addition, non-linear mapping functions are developed that relate the metric values to predicted mean opinion scores (MOS) and account for saturations in the HVS. The evaluation of prediction performance of NHIQM and the -norm reveals their excellent correlation with human perception in terms of accuracy, monotonicity, and consistency. This holds not only for the prediction performance on images taken for the training of the metrics but also for the generalization to unknown images. In addition, it is shown that the NHIQM approach and the perceptual relevance weighted -norm outperform other prominent objective quality metrics in prediction performance. Key words:Objective perceptual image quality, Normalized hybrid image quality metric, Perceptual relevance weighted -norm, Reduced-reference, Wireless imaging * Corresponding author.Email addresses: ulrich.engelke@bth.se (Ulrich Enge...

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Detection of blocking artifacts in compressed video

Cited by 74 publications

References 3 publications

Visual metrics for the evaluation of sensor data quality in outdoor perception

Visual metrics for the evaluation of sensor data quality in outdoor perception

Video Quality Metrics

Reduced-reference metric design for objective perceptual quality assessment in wireless imaging

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