Literatura académica sobre el tema "Content Based Texture Coding (CBTC)"
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Artículos de revistas sobre el tema "Content Based Texture Coding (CBTC)"
HOSUR, PRABHUDEV y ROLANDO CARRASCO. "ENHANCED FRAME-BASED VIDEO CODING TO SUPPORT CONTENT-BASED FUNCTIONALITIES". International Journal of Computational Intelligence and Applications 06, n.º 02 (junio de 2006): 161–75. http://dx.doi.org/10.1142/s1469026806001939.
Texto completoZhang, Qiuwen, Shuaichao Wei y Rijian Su. "Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC". Scientific Programming 2019 (15 de enero de 2019): 1–13. http://dx.doi.org/10.1155/2019/1574081.
Texto completoChen, Yan-Hong, Chin-Chen Chang, Chia-Chen Lin y Cheng-Yi Hsu. "Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization". Symmetry 11, n.º 1 (27 de diciembre de 2018): 21. http://dx.doi.org/10.3390/sym11010021.
Texto completoDumitras, A. y B. G. Haskell. "An Encoder–Decoder Texture Replacement Method With Application to Content-Based Movie Coding". IEEE Transactions on Circuits and Systems for Video Technology 14, n.º 6 (junio de 2004): 825–40. http://dx.doi.org/10.1109/tcsvt.2004.828336.
Texto completoWANG, XING-YUAN y YAHUI LANG. "A FAST FRACTAL ENCODING METHOD BASED ON FRACTAL DIMENSION". Fractals 17, n.º 04 (diciembre de 2009): 459–65. http://dx.doi.org/10.1142/s0218348x09004491.
Texto completoHan, Xinying, Yang Wu y Rui Wan. "A Method for Style Transfer from Artistic Images Based on Depth Extraction Generative Adversarial Network". Applied Sciences 13, n.º 2 (8 de enero de 2023): 867. http://dx.doi.org/10.3390/app13020867.
Texto completoDeep, G., J. Kaur, Simar Preet Singh, Soumya Ranjan Nayak, Manoj Kumar y Sandeep Kautish. "MeQryEP: A Texture Based Descriptor for Biomedical Image Retrieval". Journal of Healthcare Engineering 2022 (11 de abril de 2022): 1–20. http://dx.doi.org/10.1155/2022/9505229.
Texto completoXu, Jianming, Weichun Liu, Yang Qin y Guangrong Xu. "Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network". BioMed Research International 2022 (18 de julio de 2022): 1–10. http://dx.doi.org/10.1155/2022/3543531.
Texto completoXing, Qiang, Jie Chen, Jieyu Liu y Baifeng Song. "A Double Random Matrix Design Model for Fractal Art Patterns Based on Visual Characteristics". Mathematical Problems in Engineering 2022 (31 de agosto de 2022): 1–11. http://dx.doi.org/10.1155/2022/5376587.
Texto completoJenks, Robert A., Ashkan Vaziri, Ali-Reza Boloori y Garrett B. Stanley. "Self-Motion and the Shaping of Sensory Signals". Journal of Neurophysiology 103, n.º 4 (abril de 2010): 2195–207. http://dx.doi.org/10.1152/jn.00106.2009.
Texto completoTesis sobre el tema "Content Based Texture Coding (CBTC)"
Jain, Anurag. "Content-Based Texture Analysis and Synthesis for Low Bit-Rate Video Coding Using Perceptual Models". Thesis, 2006. https://etd.iisc.ac.in/handle/2005/4990.
Texto completoYeh, Li-chun y 葉立群. "Content-Based Video Coding Using Texture Analysis and Synthesis". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/18870018206430283483.
Texto completo國立成功大學
電機工程學系碩博士班
95
With age of digital-content coming, the issue from the past has continued to study have focused on how to improve picture and video compression. In the pursuit of compression ratio, the image or video is often lost visual quality. In distortion compression, the researcher pursuit balance between compression ratio and visual quality. Do not balance the two? Here, we present a method use of the existing video compression framework (H.264 codec), in addition to the general target: improved compression ratio. In encoder terminal, the input video background texture are analyzed, and then be removed. In the decoder terminal, texture is synthesized to retrieve the background. By this way, we could increase the compression ration and produce better visual quality. In this thesis, we present an effective algorithm for texture occupied certain percentage area image or video. First, we segment texture in the image, and then to analysis color, texture characteristic, replace the background texture. Continuing, we remove broken, small region. Reservations texture characteristics, such as location, colors and so on. In the decoder, we propose another synthesis method. In the past, the most use parameters to synthesize the background texture. In addition to the synthesis method can more effectively synthesize the structural background texture. This will increase effectiveness after using texture replaced.
Ndjiki-Nya, Patrick [Verfasser]. "Mid-level content based video coding using texture analysis and synthesis / von Patrick Ndjiki-Nya". 2008. http://d-nb.info/989637751/34.
Texto completoActas de conferencias sobre el tema "Content Based Texture Coding (CBTC)"
Shrinivasacharya, Purohit y M. V. Sudhamani. "Content based image retrieval system using texture and modified block truncation coding". En 2013 International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE, 2013. http://dx.doi.org/10.1109/icaccs.2013.6938770.
Texto completoMachhour, Naoufal y M'barek Nasri. "New Color and Texture Features Coding Method Combined to the Simulated Annealing Algorithm for Content Based Image Retrieval". En 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS). IEEE, 2020. http://dx.doi.org/10.1109/icds50568.2020.9268679.
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