Добірка наукової літератури з теми "Content Based Texture Coding (CBTC)"
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Статті в журналах з теми "Content Based Texture Coding (CBTC)"
HOSUR, PRABHUDEV, and ROLANDO CARRASCO. "ENHANCED FRAME-BASED VIDEO CODING TO SUPPORT CONTENT-BASED FUNCTIONALITIES." International Journal of Computational Intelligence and Applications 06, no. 02 (June 2006): 161–75. http://dx.doi.org/10.1142/s1469026806001939.
Повний текст джерелаZhang, Qiuwen, Shuaichao Wei, and Rijian Su. "Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC." Scientific Programming 2019 (January 15, 2019): 1–13. http://dx.doi.org/10.1155/2019/1574081.
Повний текст джерелаChen, Yan-Hong, Chin-Chen Chang, Chia-Chen Lin, and Cheng-Yi Hsu. "Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization." Symmetry 11, no. 1 (December 27, 2018): 21. http://dx.doi.org/10.3390/sym11010021.
Повний текст джерелаDumitras, A., and 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, no. 6 (June 2004): 825–40. http://dx.doi.org/10.1109/tcsvt.2004.828336.
Повний текст джерелаWANG, XING-YUAN, and YAHUI LANG. "A FAST FRACTAL ENCODING METHOD BASED ON FRACTAL DIMENSION." Fractals 17, no. 04 (December 2009): 459–65. http://dx.doi.org/10.1142/s0218348x09004491.
Повний текст джерелаHan, Xinying, Yang Wu, and Rui Wan. "A Method for Style Transfer from Artistic Images Based on Depth Extraction Generative Adversarial Network." Applied Sciences 13, no. 2 (January 8, 2023): 867. http://dx.doi.org/10.3390/app13020867.
Повний текст джерелаDeep, G., J. Kaur, Simar Preet Singh, Soumya Ranjan Nayak, Manoj Kumar, and Sandeep Kautish. "MeQryEP: A Texture Based Descriptor for Biomedical Image Retrieval." Journal of Healthcare Engineering 2022 (April 11, 2022): 1–20. http://dx.doi.org/10.1155/2022/9505229.
Повний текст джерелаXu, Jianming, Weichun Liu, Yang Qin, and Guangrong Xu. "Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network." BioMed Research International 2022 (July 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/3543531.
Повний текст джерелаXing, Qiang, Jie Chen, Jieyu Liu, and Baifeng Song. "A Double Random Matrix Design Model for Fractal Art Patterns Based on Visual Characteristics." Mathematical Problems in Engineering 2022 (August 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/5376587.
Повний текст джерелаJenks, Robert A., Ashkan Vaziri, Ali-Reza Boloori, and Garrett B. Stanley. "Self-Motion and the Shaping of Sensory Signals." Journal of Neurophysiology 103, no. 4 (April 2010): 2195–207. http://dx.doi.org/10.1152/jn.00106.2009.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаYeh, Li-chun, and 葉立群. "Content-Based Video Coding Using Texture Analysis and Synthesis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/18870018206430283483.
Повний текст джерела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.
Повний текст джерелаТези доповідей конференцій з теми "Content Based Texture Coding (CBTC)"
Shrinivasacharya, Purohit, and M. V. Sudhamani. "Content based image retrieval system using texture and modified block truncation coding." In 2013 International Conference on Advanced Computing & Communication Systems (ICACCS). IEEE, 2013. http://dx.doi.org/10.1109/icaccs.2013.6938770.
Повний текст джерелаMachhour, Naoufal, and M'barek Nasri. "New Color and Texture Features Coding Method Combined to the Simulated Annealing Algorithm for Content Based Image Retrieval." In 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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