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RISS 인기검색어
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- 저자 : ( Anhong Wang ) (검색결과 5 건)
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Scalable Coding of Depth Images with Synthesis-Guided Edge Detection
( Lijun Zhao ),( Anhong Wang ),( Bing Zeng ),( Jian Jin ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.10
This paper presents a scalable coding method for depth images by considering the quality of synthesized images in virtual views. First, we design a new edge detection algorithm that is based on calculating the depth difference between two neighboring pixels within the depth map. By choosing different thresholds, this algorithm generates a scalable bit stream that puts larger depth differences in front, followed by smaller depth differences. A scalable scheme is also designed for coding depth pixels through a layered sampling structure. At the receiver side, the full-resolution depth image is reconstructed from the received bits by solving a partial-differential-equation (PDE). Experimental results show that the proposed method improves the rate-distortion performance of synthesized images at virtual views and achieves better visual quality.
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Adaptive High-order Variation De-noising Method for Edge Detection with Wavelet Coefficients
Chenghua Liu,Anhong Wang 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.2
This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection.
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( Haiwei Lei ),( Wenyi Liu ),( Anhong Wang ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.3
The deblocking filter (DF) reduces blocking artifacts in encoded video sequences, and thereby significantly improves the subjective and objective quality of videos. Statistics show that the DF accounts for 5-18% of the total decoding time in high-efficiency video coding. Therefore, speeding up the DF will improve codec performance, especially for the decoder. In view of the rapid development of multicore technology, we propose a parallel DF scheme based on a modified order of accessing the coding tree units (CTUs) by analyzing the data dependencies between adjacent CTUs. This enables the DF to run in parallel, providing accelerated performance and more flexibility in the degree of parallelism, as well as finer parallel granularity. We additionally solve the problems of variable privatization and thread synchronization in the parallelization of the DF. Finally, the DF module is parallelized based on the HM16.1 reference software using OpenMP technology. The acceleration performance is experimentally tested under various numbers of cores, and the results show that the proposed scheme is very effective at speeding up the DF.
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Color-Image Guided Depth Map Super-Resolution Based on Iterative Depth Feature Enhancement
( Lijun Zhao ),( Ke Wang ),( Jinjing Zhang ),( Jialong Zhang ),( Anhong Wang ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.8
With the rapid development of deep learning, Depth Map Super-Resolution (DMSR) method has achieved more advanced performances. However, when the upsampling rate is very large, it is difficult to capture the structural consistency between color features and depth features by these DMSR methods. Therefore, we propose a color-image guided DMSR method based on iterative depth feature enhancement. Considering the feature difference between high-quality color features and low-quality depth features, we propose to decompose the depth features into High-Frequency (HF) and Low-Frequency (LF) components. Due to structural homogeneity of depth HF components and HF color features, only HF color features are used to enhance the depth HF features without using the LF color features. Before the HF and LF depth feature decomposition, the LF component of the previous depth decomposition and the updated HF component are combined together. After decomposing and reorganizing recursively-updated features, we combine all the depth LF features with the final updated depth HF features to obtain the enhanced-depth features. Next, the enhanced-depth features are input into the multi-stage depth map fusion reconstruction block, in which the cross enhancement module is introduced into the reconstruction block to fully mine the spatial correlation of depth map by interleaving various features between different convolution groups. Experimental results can show that the two objective assessments of root mean square error and mean absolute deviation of the proposed method are superior to those of many latest DMSR methods.
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A Common Bitmap Block Truncation Coding for Color Images Based on Binary Ant Colony Optimization
( Zhihong Li ),( Qiang Jin ),( Chin-chen Chang ),( Li Liu ),( Anhong Wang ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.5
For the compression of color images, a common bitmap usually is generated to replace the three individual bitmaps that originate from block truncation coding (BTC) of the R, G and B channels. However, common bitmaps generated by some traditional schemes are not the best possible because they do not consider the minimized distortion of the entire color image. In this paper, we propose a near-optimized common bitmap scheme for BTC using Binary Ant Colony Optimization (BACO), producing a BACO-BTC scheme. First, the color image is compressed by the BTC algorithm to get three individual bitmaps, and three pairs of quantization values for the R, G, and B channels. Second, a near-optimized common bitmap is generated with minimized distortion of the entire color image based on the idea of BACO. Finally, the color image is reconstructed easily by the corresponding quantization values according to the common bitmap. The experimental results confirmed that reconstructed image of the proposed scheme has better visual quality and less computational complexity than the referenced schemes.
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