Efficient video transmission over unreliable channels may encounter huge challenge due to unavoidable bit error or packets loss. Error concealment (EC) techniques at the decoder side have been developed to recover the...
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The H.264 video coding standard has a significant performance benefit versus former standards. However, motion compensation decoding process takes up a significant decoding time due to the advanced coding features. In...
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The H.264 video coding standard has a significant performance benefit versus former standards. However, motion compensation decoding process takes up a significant decoding time due to the advanced coding features. In this paper, the theory of motion compensation decoding in H.264 is given at first. Then the improved method is proposed by motion compensation fast decoding algorithm and optimization based on ADI Blackfin533 hardware platform. Both PC and ADI Blackfin533 experimental results show that the proposed optimization strategy can substantially improve the efficiency of real-time H.264 decoder.
Example-based face sketch synthesis technology generally requires face photo-sketch images with face alignment and size *** break through the limitation,we propose a global face sketch synthesis method:In training,all...
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Example-based face sketch synthesis technology generally requires face photo-sketch images with face alignment and size *** break through the limitation,we propose a global face sketch synthesis method:In training,all training photo-sketch patch pairs are collected together and a photo feature dictionary is learned from the photo *** each atom of the dictionary,its K closest photo-sketch patch pairs are clustered,namely "Anchored Neighborhood".In testing,for each test photo patch,we search its nearest photo patch in the Anchored Neighborhood determined by its closest atom,then the corresponding sketch patch is the *** the same way,we train and test in the high-frequency domain and synthesis the high-frequency ***,the fusion of the initial and the high-frequency results is the final *** experiments on three public face sketch datasets and various real-world photos demonstrate the effectiveness and robustness of the proposed method.
Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades. However, matching noise within and between images, along with inconsisten...
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ISBN:
(数字)9798350353006
ISBN:
(纸本)9798350353013
Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades. However, matching noise within and between images, along with inconsistent key-point detection, frequently degrades the matching performance. We review these problems and thus propose: 1) a novel and unified Filtering and Calibrating (FC) approach that jointly rejects outliers and optimizes inliers, and 2) leveraging both the matching context and the underlying image texture to remove matching uncertainties. Under the guidance of the above innovations, we construct Filtering and Calibrating Graph Neural Network (FC-GNN), which follows the FC approach to recover reliable and accurate correspondences from various interferences. FC-GNN conducts an effectively combined inference of contextual and local information through careful embedding and multiple information aggregations, predicting confidence scores and calibration offsets for the input correspondences to jointly filter out outliers and improve pixel-level matching accuracy. Moreover, we exploit the local coherence of matches to perform inference on local graphs, thereby reducing computational complexity. Overall, FC-GNN operates at lightning speed and can greatly boost the performance of diverse matching pipelines across various tasks, showcasing the immense potential of such approaches to become standard and pivotal components of image matching. Code is avaiable at https://***/xuy123456/fcgnn.
In this paper,a novel methodology is presented to settle the region of interest(ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with colo...
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In this paper,a novel methodology is presented to settle the region of interest(ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with color *** order to make full use of the local color and spatial information,vehicle images are divided into different superpixels at *** spatial relationship between superpixels and the outermost pixels is then used for the background removal of vehicle *** comparing with the vehicle window clustering centroids obtained by k-means,the superpixels close to the universal color characteristics of windows are removed so that the dominant color superpixels are ***,a linear Support Vector Machine classifier is trained for color *** experiments demonstrate that the proposed methodology is effective for color region of interest detection and thus contribute to vehicle color recognition.
An effective algorithm for global abnormal detection from surveillance video is proposed in this paper. The algorithm is based on sparse representation. To deal with the illumination change in video scenes, specific f...
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ISBN:
(纸本)9781510830981
An effective algorithm for global abnormal detection from surveillance video is proposed in this paper. The algorithm is based on sparse representation. To deal with the illumination change in video scenes, specific feature extract methods are designed for corresponding illumination conditions. In the case of non-uniform illumination, features are extracted directly on the original image;in the case of uniform illumination, features are extracted on the binary image obtained by threshold segmentation on the difference image, where the thresholds are computed by the Otsu's method. The features extracted on normal video are used to learn an over-complete dictionary. Then, the sparse reconstruction cost over the dictionary is used to detect abnormal events. Experiments on the open global abnormal dataset and the comparison to the state-of-the-art methods validate effectiveness and quickness of our algorithm.
In this paper, a new fast encoding scheme using CU depth information for quality scalable HEVC was proposed. The proposed scheme based on the correlation between the base layer and enhancement layer, as well as the co...
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In this paper, a new fast encoding scheme using CU depth information for quality scalable HEVC was proposed. The proposed scheme based on the correlation between the base layer and enhancement layer, as well as the correlation between current frame and reference frame, which obtained by experiment. The main idea of the algorithm is that when each LCU is encoded in enhancement layer, it's corresponding encoded LCU's best coding depth in base layer is used as the new maximum encoding depth in enhancement layer, thereby, it can reduce the unnecessary CU division process then reduce the computational complexity. On the other hand, for base layer, when each LCU encoding in current frame, the corresponding LCU's depth in already coded reference frame is utilized to skip parts process of mode selection before reach this reference depth, so that the computational complexity can be reduced again. Experimental results show that compared with the original SHM5.1, our proposed method saves about 59.45% and 67.71% encoding time in whole encoding layers and enhancement layer respectively, with a negligible decrease in PSNR of 0.014 dB and bitrate increase of 0.46% in EL, as well as PSNR of 0.05 dB and bitrate increase of 1.15% in BL averagely.
To alleviate the encoder computation load in inter-frame coding of the High Efficiency Video Coding (HEVC), a new fast inter-frame prediction mode decision method is proposed by reducing the number of execution of the...
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To alleviate the encoder computation load in inter-frame coding of the High Efficiency Video Coding (HEVC), a new fast inter-frame prediction mode decision method is proposed by reducing the number of execution of the advanced motion vector prediction (AMVP) mode. Based on the correlation analysis between the corresponding located CU (Coding Unit) in the previous frame and the current CU, we first select the best one inter prediction mode by just performing merge mode decision process and then only perform the AMVP process on the selected one. Experimental results show that the proposed scheme provides 33.04% encoder time savings in Random Access (RA) configuration and 38.05% time savings in Low Delay (LD) configuration on average, and its increment in bit rate is 0.87% in RA and 1.29% in LD compared to the default encoding scheme in HM10.0.
Disparity estimation is an important technique in stereo video coding. This paper presents a disparity estimation algorithm based on edge detection. The algorithm makes full use of the human visual characteristics, th...
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Disparity estimation is an important technique in stereo video coding. This paper presents a disparity estimation algorithm based on edge detection. The algorithm makes full use of the human visual characteristics, that is, the human eye is more sensitive to the distortion of the edge region. Therefore, joint estimation is used for edge detection. The large code block size for coding the background region and the flat areas while small size for coding the edge region were used in this paper. Compared to the disparity estimation algorithm proposed in, the proposed algorithm can greatly improve the encoding speed of stereo video without affecting subjective image quality.
Detection of groups of interacting people is a difficult task, especially in an unconstrained and crowded environment. In this paper, as a main contribution, we present a novel and efficient framework for social group...
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