Region-of-Interest Based Conversational HEVC Coding with Hierarchical Perception Model of Face

Xu, Mai; Deng, Xin; Li, Shengxi; Wang, Zulin

doi:10.1109/jstsp.2014.2314864

Cited by 88 publications

(43 citation statements)

References 29 publications

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“…Our model trained on different large dataset like SFU dataset [18] and HEVC video_database [19], [31][32]. Here, we have shown results only for HEVC video_database [19].…”

Section: Results and Analysismentioning

confidence: 94%

“…For every frame, Motion Vectors (MV) and SRF (Smooth Residual Feature) and BCM (Block Coding Maps) generated. Here, RaceHorseD video data taken to obtain saliency map from HEVC video_database [19], [31][32]. Both the features are necessary to obtain a saliency map of an input video.…”

Section: Proposed Saliency Estimationmentioning

confidence: 99%

“…We have implemented our extensive experiments on large video SFU dataset [18] and HEVC video_database [19], [31][32]. In modern era, the availability of 4K monitors is highly increased.…”

Section: Implementation Detailsmentioning

confidence: 99%

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Efficient High Quality Video Assessment Using Salient Features

Rekha¹,

Kumar²

2017

IJIVP

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High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have introduced an efficient video compression technique as modified HEVC coding based on saliency features to counter these existing drawbacks. We highlight first, on extracting features on the raw data and then compressed it largely. This technique makes our model powerful and provides effective performance in terms of compression. Our experiment results proves that our model provide better efficiency in terms of average PSNR, MSE and bitrate. Our experimental results outperforms all the existing techniques in terms of saliency map detection, AUC, NSS, KLD and JSD. The average AUC, NSS and KLD value by our proposed method are 0.846, 1.702 and 0.532 respectively which is very high compare to other existing technique.

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“…Our model trained on different large dataset like SFU dataset [18] and HEVC video_database [19], [31][32]. Here, we have shown results only for HEVC video_database [19].…”

Section: Results and Analysismentioning

confidence: 94%

Section: Proposed Saliency Estimationmentioning

confidence: 99%

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Efficient High Quality Video Assessment Using Salient Features

Rekha¹,

Kumar²

2017

IJIVP

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“…The concept of ROI is employed in many works on image processing, such as [9][10][11]. A ROI corresponds to a certain area of an image or a scene that is of particular interest to the user or the system, such as the face of every person, an individual or a moving object [9,10].…”

Section: Region Of Interest and Statistical Analysismentioning

confidence: 99%

“…A ROI corresponds to a certain area of an image or a scene that is of particular interest to the user or the system, such as the face of every person, an individual or a moving object [9,10]. Generally, these works use the ROI to extract relevant information for the viewer that is not interested in the remaining areas of the image (which can be discarded or processed in a different manner), such as in the case of surveillance and medical video processing.…”

Section: Region Of Interest and Statistical Analysismentioning

confidence: 99%

Complexity reduction of 3D-HEVC based on depth analysis for background and ROI classification

Avila

Conceição

Bubolz

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The 3D extension of the High Efficiency Video Coding (HEVC) standard achieves large compression rates thanks to the addition of several tools to encode multiview and depth information on top of those available in HEVC. The use of such tools incur in a very large computational demand, which can be a serious problem in power and computationallyconstrained devices and applications. However, not all information contained in an image is fundamental to the viewer, so that different levels of computational effort can be employed when encoding different image regions. The Region of Interest (ROI) concept is used in this work to classify each Coding Unit (CU) as foreground, heterogeneous background and homogeneous background. Then, a simplified encoding process is employed in those regions classified as homogeneous background, terminating earlier the partitioning process in texture CUs, while still still performing the regular decisions in areas classified as ROI. Experimental results show an average reduction of 22.6% in computational complexity for texture coding with negligible or non-perceived image quality degradation.

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A sensitive object‐oriented approach to big surveillance data compression for social security applications in smart cities

et al. 2016

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Surveillance has become a fairly common practice with the global boom in "smart cities". How to efficiently store and manage the vast quantities of surveillance data is a persistent challenge in terms of analyzing social security problems. Developing data compression technology under the analytic requirements of surveillance data is the key to solving the storage problem. Criminal investigation demands the quality preservation of sensitive objects, typically pedestrians, human faces, vehicles, and license plates; however, the analytical value of surveillance data is rapidly lost as the compression ratio increases. In this paper, we propose a sensitive object-oriented regions of interest-based coding strategy for preserving the analytical value of surveillance data. In the proposed method, instead of generating a saliency map based on human visual perception, we consider saliency as a set of characteristics important for object detection and recognition. By making this modification, almost all sensitive objects necessary in a criminal investigation are assigned high saliency value rather than only one or two salient regions. Motions in the temporal domain are integrated to place emphasis on moving objects, namely moving sensitive objects, which then gain the highest saliency. Finally, a saliency-based rate control algorithm embedded in High Efficiency Video Coding is used to maintain the quality of sensitive objects in the encoded video under a fixed bitrate. Experiments were conducted on two analytical indexes: Feature similarity and object detection accuracy. The results showed that by achieving the same feature similarity and object detection accuracy, our method can save 20% and 40% bitrate over High Efficiency Video Coding, respectively, for the storage of big surveillance data. be archived. Depending on the retention policy, video streams (which are in increasingly higher resolution) can consume storage rapidly, leading to high financial pressure to scale the storage infrastructure. To relieve the financial pressure, normally a high compression ratio is adopted to make a trade-off between the retention period and the video quality. However, according to previous research 1, the reduced quality of surveillance video severely degrades the performance in video analysis tasks such as object recognition and tracking. This makes critical applications such as large crowd management in public hotspot areas very unreliable 2.Our ability to maintain video analysis performance while resolving the surveillance video storage problem is dependent on our ability to accurately and effectively account for the characteristics of the videos. In normal applications, surveillance videos are used for analytical purposes. Taking a typical criminal investigation application, for instance, the analytically sensitive objects (e.g., pedestrians and vehicles) in the videos have much higher importance than the background. Therefore, it is reasonable to divide surveillance video frames into regions of interest (ROIs) and non-ROIs where R...

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Region-of-Interest Based Conversational HEVC Coding with Hierarchical Perception Model of Face

Cited by 88 publications

References 29 publications

Efficient High Quality Video Assessment Using Salient Features

Efficient High Quality Video Assessment Using Salient Features

Complexity reduction of 3D-HEVC based on depth analysis for background and ROI classification

A sensitive object‐oriented approach to big surveillance data compression for social security applications in smart cities

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