Multiple Priority Region of Interest Coding with H.264

Agrafiotis, Dimitris; Bull, David; Canagarajah, N.; Kamnoonwatana, Nawat

doi:10.1109/icip.2006.313201

Cited by 26 publications

(5 citation statements)

References 6 publications

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“…While RoI-based video coding is probably the most used application for deriving RoIs, e.g., in [4,5], using the RoI in error resilience models has also been investigated. In all of the following research works, RoI-based models have been coupled with an H.264/AVC error resilience feature, namely 978-1-4244-4652-0/09/$25.00 ©2009 IEEE FMO.…”

Section: Related Workmentioning

confidence: 99%

A new H.264/AVC error resilience model based on Regions of Interest

Boulos

Chen

Parrein

et al. 2009

2009 17th International Packet Video Workshop

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Video transmission over the Internet can sometimes be subject to packet loss which reduces the end-user's Quality of Experience (QoE). Solutions aiming at improving the robustness of a video bitstream can be used to subdue this problem. In this paper, we propose a new Region of Interest-based error resilience model to protect the most important part of the picture from distortions. We conduct eye tracking tests in order to collect the Region of Interest (RoI) data. Then, we apply in the encoder an intra-prediction restriction algorithm to the macroblocks belonging to the RoI. Results show that while no significant overhead is noted, the perceived quality of the video's RoI, measured by means of a perceptual video quality metric, increases in the presence of packet loss compared to the traditional encoding approach.Index Terms-Eye tracking, region of interest, packet loss, error resilience, perceptual quality.

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Section: Related Workmentioning

confidence: 99%

A new H.264/AVC error resilience model based on Regions of Interest

Boulos

Chen

Parrein

et al. 2009

2009 17th International Packet Video Workshop

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“…Ou et al [5] propose a structural similarity (SSIM)-based rate control algorithm, which assumes that the SSIM index [6] correlates better with the perceived video quality than mean squared error (MSE) and achieves up to a 32% bit rate reduction compared with the JM reference software [7] at the same quality. Agrafiotis et al [8] and Chiang et al [9] detect regions of interest and assign the available bits to the regions according to their importance. Zhang et al [10] proposes a two-pass rate control scheme for high-definition videos.…”

Section: Introductionmentioning

confidence: 99%

A Novel Rate Control Framework for SIFT/SURF Feature Preservation in H.264/AVC Video Compression

Chao

Huitl

Steinbach

et al. 2015

IEEE Trans. Circuits Syst. Video Technol.

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This paper presents a novel rate control framework for H.264/Advanced Video Coding-based video coding that improves the preservation of gradient-based features like scale-invariant feature transform or speeded up robust feature compared with the default rate control algorithm in the JM reference software. First, a criterion (matching score) for feature preservation on the basis of the bag-of-features concept is proposed. Then, the matching scores are collected as a function of the quantization parameters and analyzed for different feature types. With this analysis, macroblocks are categorized into different groups before encoding. Our rate control algorithm assigns different quantization parameters to each group according to the importance of the group for feature extraction. The experimental results show that our rate control algorithm achieves the desired target bit rate, and more features are preserved compared with videos encoded using the default rate control. The proposed approach not only improves feature preservation, but also leads to a noticeable performance improvement in a real image retrieval system. The rate control framework proposed in this paper is fully standard compatible.Index Terms-Bag-of-features (BoFs), evaluation, H.264/Advanced Video Coding (AVC), rate control, scale-invariant feature transform (SIFT), speeded up robust feature (SURF).

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“…Artifacts introduced by coding algorithms may affect the accuracy of several applications [1]. Several works in the literature aim at adapting both image [10] and video [11,12] compression architectures so that the quality local features is preserved.…”

Section: Introductionmentioning

confidence: 99%

Hybrid coding of visual content and local image features

Baroffio

Cesana

Redondi

et al. 2015

2015 IEEE International Conference on Image Processing (ICIP)

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Distributed visual analysis applications, such as mobile visual search or Visual Sensor Networks (VSNs) require the transmission of visual content on a bandwidth-limited network, from a peripheral node to a processing unit. Traditionally, a "Compress-Then-Analyze" approach has been pursued, in which sensing nodes acquire and encode the pixel-level representation of the visual content, that is subsequently transmitted to a sink node in order to be processed. This approach might not represent the most effective solution, since several analysis applications leverage a compact representation of the content, thus resulting in an inefficient usage of network resources. Furthermore, coding artifacts might significantly impact the accuracy of the visual task at hand. To tackle such limitations, an orthogonal approach named "Analyze-Then-Compress" has been proposed [1]. According to such a paradigm, sensing nodes are responsible for the extraction of visual features, that are encoded and transmitted to a sink node for further processing. In spite of improved task efficiency, such paradigm implies the central processing node not being able to reconstruct a pixel-level representation of the visual content. In this paper we propose an effective compromise between the two paradigms, namely "Hybrid-Analyze-Then-Compress" (HATC) that aims at jointly encoding visual content and local image features. Furthermore, we show how a target tradeoff between image quality and task accuracy might be achieved by accurately allocating the bitrate to either visual content or local features.

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Multiple Priority Region of Interest Coding with H.264

Cited by 26 publications

References 6 publications

A new H.264/AVC error resilience model based on Regions of Interest

A new H.264/AVC error resilience model based on Regions of Interest

A Novel Rate Control Framework for SIFT/SURF Feature Preservation in H.264/AVC Video Compression

Hybrid coding of visual content and local image features

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