Surveillance video coding with quadtree partition based ROI extraction

Xing, Peiyin; Huang, Tiejun; Gao, Wen

doi:10.1109/pcs.2013.6737707

Cited by 14 publications

(5 citation statements)

References 4 publications

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“…Xing et al [3] proposed ROI extraction-based image coding with HEVC [4]. In their proposed method, the background value is calculated based on the segment-and-weight-based running average method [5], [6] and the foreground mask is produced by using the Gaussian Mixture Model.…”

Section: ) Roi-based Image Compression Researchesmentioning

confidence: 99%

Versatile Video Coding-Based Coding Tree Unit Level Image Compression With Dual Quantization Parameters for Hybrid Vision

2023

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Image analysis based on machine vision is hugely manipulated in the smart industry. Good-quality images are required for outstanding machine analysis results, but handling high-definition images could be problematic in a constrained environment such as a low-bandwidth network or low-capacity storage. Lowering the image resolution might be a straightforward solution for reducing image data, but it would occasion much information loss, leading to the deterioration of machine vision. Moreover, human supervision could be necessary for a contingency that machine vision cannot control.Therefore, an innovative image compression method considering machine and human vision is required; more compression efficiency than the state-ofthe-art codec, praiseworthy machine vision performance, and human-recognizable quality. In this paper, we propose Versatile video coding(VVC) based image compression for hybrid vision, i.e., machine vision and human vision. Our work provides a coding tree unit(CTU) level image compression with dual quantization parameters (QPs) according to the quantization parameter map and the saliency extracted by the object detection network; in the salient region, the proposed method maintains high quality with low QP but degrades the quality with high QP in the non-salient region.Compared with VVC, the proposed compression method achieves a bitrate reduction of up to 25.5% in machine vision tasks, proving more compression efficiency and still admirable machine vision performance. From the perspective of human vision, the proposed method provides human-perceptible image quality, preserving acceptable objective quality values.

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Section: ) Roi-based Image Compression Researchesmentioning

confidence: 99%

Versatile Video Coding-Based Coding Tree Unit Level Image Compression With Dual Quantization Parameters for Hybrid Vision

2023

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“…Experimental results demonstrate that the visual quality of the face, in particular, facial features, can be enhanced by their approach. In order to reduce the coding cost of a surveillance video, Xing et al [14] present a method to encode the foreground objects and the background separately. They perform a ROI extraction method following the block partition in the HEVC's quadtree structure.…”

Section: A Conversational or Surveillance Video Codingmentioning

confidence: 99%

Perceptual-Based HEVC Intra Coding Optimization Using Deep Convolution Networks

Sun

Zuo

et al. 2019

IEEE Access

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In this paper, we propose a novel perceptual-based intra coding optimization algorithm for the High Efficiency Video Coding (HEVC) using deep convolution networks (DCNs). According to the saliency map, the algorithm can intelligently adjust bit rate allocation between the salient and non-salient regions of the video. The proposed strategy mainly consists of two techniques, saliency map extraction, and intelligent bit rate allocation. First, we train a DCN model to generate the saliency map that highlights semantically salient regions. Compared with the texture-based region of interest (ROI) extraction techniques, our model is more consistent with the human visual system (HVS). Second, based on the saliency map, a modified rate-distortion optimization (RDO) method is designed to adaptively adjust bit rate allocation. As a result, the quality of the salient regions will be improved by allocating more bits while allocating fewer bit rates for the non-salient regions. The experimental results demonstrate that our approach can deal with multiple types of video to enhance the visual experience. For conventional videos, the proposed method achieves 0.64-dB PSNR improvement for the salient regions and saves 3.02% bit rate on average compared with HM16.7. Moreover, for conversational videos, the proposed method can significantly reduce the bit rate by 8.65% without dropping the quality of important regions.

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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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Surveillance video coding with quadtree partition based ROI extraction

Cited by 14 publications

References 4 publications

Versatile Video Coding-Based Coding Tree Unit Level Image Compression With Dual Quantization Parameters for Hybrid Vision

Versatile Video Coding-Based Coding Tree Unit Level Image Compression With Dual Quantization Parameters for Hybrid Vision

Perceptual-Based HEVC Intra Coding Optimization Using Deep Convolution Networks

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

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