In the era of cloud computing, there are many correlated images in the cloud, joint compression of these images may provide much higher compression ratio than individual coding. Model-based coding is an appealing appr...
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ISBN:
(纸本)9781479986897
In the era of cloud computing, there are many correlated images in the cloud, joint compression of these images may provide much higher compression ratio than individual coding. Model-based coding is an appealing approach to image coding in the cloud, as it removes knowledge redundancy among images that share the same model. In this paper, we make an attempt to model-based image coding for landmark images, where our model consists of three-dimensional (3-D) point-cloud plus image patches to describe the geometry and surface color of the landmark respectively. The camera parameters of an input image are estimated based on the 3-D point-cloud and the patches in the model, and then prediction image is generated by selecting, warping, and stitching image patches as well as illuminance compensation, the residue between original and prediction images is compressed by P-frame coding in HEVC encoder. We perform experiments on an Internet photo collection to verify the effectiveness of the proposed scheme. Preliminary results display the superior performance of our scheme that achieves as high as 39.9% bits saving compared to HEVC intra on a single image. The proposed scheme indicates a promising approach to image coding in the cloud and is worthy of in-depth investigation.
Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing fea...
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In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical ...
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In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical parameter setting limits its performance. In this paper, we propose a novel Adaptive Weighted Total Generalized Variation model for image restoration. We analyze the TGV model from Bayesian Probability view and derive a novel adaptive parameter calculation scheme for it, exploiting the image's self-similarity. Experiment results on image deblurring and reconstruction show that by adapting the parameters in TGV model to image contents, the proposed model can restore image's edges and details well and achieve significant improvement over state of the art variational based models.
Bundle adjustment with additional parameters is identified as a critical step for precise orthoimage generation and 3D reconstruction of Dunhuang wall paintings. Due to the introduction of self-calibration parameters ...
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The scalable and multiview extensions of the High Efficiency Video Coding share the same high-level syntax coding structure. For the scalable extension, the motion field of the inter-layer reference picture is modifie...
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ISBN:
(纸本)9781479983926
The scalable and multiview extensions of the High Efficiency Video Coding share the same high-level syntax coding structure. For the scalable extension, the motion field of the inter-layer reference picture is modified through Motion Field Mapping before used for motion vector prediction. However, the motion field of the inter-layer reference picture is used without modification in the multiview extension. In this paper, a disparity-compensated inter-layer motion prediction is proposed for multiview video coding to achieve disparity compensation in inter-layer motion prediction using the scaled reference layer offset. The experimental results show that comparing with the multiview extension anchor, the proposed method achieves an average of 1.1% bitrate reduction.
Network predictive control is effectively in dealing with time-delay, data dropouts, packets disorders of Network Control System. This paper studies the stochastic stability of a affine nonlinear system within random ...
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ISBN:
(纸本)9781467374439
Network predictive control is effectively in dealing with time-delay, data dropouts, packets disorders of Network Control System. This paper studies the stochastic stability of a affine nonlinear system within random time delay. The system controller is consist of a control prediction generator(CPG) at the controller side and a network delay compensator(NDC) at the plant side. Different from the previous literatures that model the random delay of the feedback channel and the forward channel as Markov chain respectively, the model in this paper is based on the round-trip delay, which making the predictive control process more concise but without losing generality. Then, this paper gives the necessary and sufficient conditions for stochastic stability of the closed-loop system. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.
Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity togethe...
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ISBN:
(纸本)9781479983926
Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity together, called group sparsity optimization. Instead of processing each image patch individually, in the proposed scheme, similar patches in one group are required to be well-represented on learned dictionary collaboratively, using group sparsity regularization. The group sparsity not only imposes every patch's representation to be sparse, bus also requires patches' coefficients in the group share the similar pattern. The experiment results on standard test images demonstrate that our scheme can improve the PSNR of the compressed images by an average of 1.25 dB, and outperform state of the art deblocking approaches.
This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and lo...
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ISBN:
(纸本)9781509033331
This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and optimally planning further aerial surveys. A label propagation mechanism is adopted to reliably propagate the labeled pixels annotated by the user on the aerial image by visual photointerpretation to the (lower resolution) satellite image. The experimental analysis is carried out using post-disaster images of the 2010 Yushu earthquake in China. The obtained results confirm that the proposed method can significantly reduce the user annotation effort and the cost for acquiring additional aerial images leading to more accurate and timely debris detection maps.
In order to adapt different scale land cover segmentation, an optimized approach under the guidance of k-means clustering for multi-scale segmentation is proposed. At first, small scale segmentation and k-means cluste...
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This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise contin...
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