Auswahl der wissenschaftlichen Literatur zum Thema „Encoder optimization“
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Zeitschriftenartikel zum Thema "Encoder optimization"
Hassan, Hammad, Muhammad Nasir Khan, Syed Omer Gilani, Mohsin Jamil, Hasan Maqbool, Abdul Waheed Malik und Ishtiaq Ahmad. „H.264 Encoder Parameter Optimization for Encoded Wireless Multimedia Transmissions“. IEEE Access 6 (2018): 22046–53. http://dx.doi.org/10.1109/access.2018.2824835.
Der volle Inhalt der QuelleHamza, Ahmed M., Mohamed Abdelazim, Abdelrahman Abdelazim und Djamel Ait-Boudaoud. „HEVC Rate-Distortion Optimization with Source Modeling“. Electronic Imaging 2021, Nr. 10 (18.01.2021): 259–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.10.ipas-259.
Der volle Inhalt der QuelleWang, Lei, Qimin Ren, Jingang Jiang, Hongxin Zhang und Yongde Zhang. „Recent Patents on Magnetic Encoder and its use in Rotating Mechanism“. Recent Patents on Engineering 13, Nr. 3 (19.09.2019): 194–200. http://dx.doi.org/10.2174/1872212112666180628145856.
Der volle Inhalt der QuelleLee, Yoon Jin, Dong In Bae und Gwang Hoon Park. „HEVC Encoder Optimization using Depth Information“. Journal of Broadcast Engineering 19, Nr. 5 (30.09.2014): 640–55. http://dx.doi.org/10.5909/jbe.2014.19.5.640.
Der volle Inhalt der QuelleWang, Shanshe, Falei Luo, Siwei Ma, Xiang Zhang, Shiqi Wang, Debin Zhao und Wen Gao. „Low complexity encoder optimization for HEVC“. Journal of Visual Communication and Image Representation 35 (Februar 2016): 120–31. http://dx.doi.org/10.1016/j.jvcir.2015.12.005.
Der volle Inhalt der QuelleMerel, Josh, Donald M. Pianto, John P. Cunningham und Liam Paninski. „Encoder-Decoder Optimization for Brain-Computer Interfaces“. PLOS Computational Biology 11, Nr. 6 (01.06.2015): e1004288. http://dx.doi.org/10.1371/journal.pcbi.1004288.
Der volle Inhalt der QuelleHanli Wang, Ming-Yan Chan, S. Kwong und Chi-Wah Kok. „Novel quantized DCT for video encoder optimization“. IEEE Signal Processing Letters 13, Nr. 4 (April 2006): 205–8. http://dx.doi.org/10.1109/lsp.2005.863691.
Der volle Inhalt der QuelleBariani, M., P. Lambruschini und M. Raggio. „An Efficient Multi-Core SIMD Implementation for H.264/AVC Encoder“. VLSI Design 2012 (29.05.2012): 1–14. http://dx.doi.org/10.1155/2012/413747.
Der volle Inhalt der QuelleCho, Jung-Hyun, Myung-Soo Lee, Han-Soo Jeong, Chang-Suk Kim und Dae-Jea Cho. „Optimization of H.264 Encoder based on Hardware Implementation in Embedded System“. Journal of the Korea Academia-Industrial cooperation Society 11, Nr. 8 (31.08.2010): 3076–82. http://dx.doi.org/10.5762/kais.2010.11.8.3076.
Der volle Inhalt der QuelleHou, Han, Guohua Cao, Hongchang Ding und Kun Li. „Research on Particle Swarm Compensation Method for Subdivision Error Optimization of Photoelectric Encoder Based on Parallel Iteration“. Sensors 22, Nr. 12 (12.06.2022): 4456. http://dx.doi.org/10.3390/s22124456.
Der volle Inhalt der QuelleDissertationen zum Thema "Encoder optimization"
Mallikarachchi, Thanuja. „HEVC encoder optimization and decoding complexity-aware video encoding“. Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/841841/.
Der volle Inhalt der QuelleSyu, Eric. „Implementing rate-distortion optimization on a resource-limited H.264 encoder“. Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33365.
Der volle Inhalt der QuelleIncludes bibliographical references (leaves 57-59).
This thesis models the rate-distortion characteristics of an H.264 video compression encoder to improve its mode decision performance. First, it provides a background to the fundamentals of video compression. Then it describes the problem of estimating rate and distortion of a macroblock given limited computational resources. It derives the macroblock rate and distortion as a function of the residual SAD and H.264 quantization parameter QP. From the resulting equations, this thesis implements and verifies rate-distortion optimization on a resource-limited H.264 encoder. Finally, it explores other avenues of improvement.
by Eric Syu.
M.Eng.
Carriço, Nuno Filipe Marques. „Transformer approaches on hyper-parameter optimization and anomaly detection with applications in stream tuning“. Master's thesis, Universidade de Évora, 2022. http://hdl.handle.net/10174/31068.
Der volle Inhalt der QuelleHägg, Ragnar. „Scalable High Efficiency Video Coding : Cross-layer optimization“. Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-257558.
Der volle Inhalt der QuelleSun, Hui [Verfasser], Ralph [Akademischer Betreuer] Kennel, Alexander W. [Gutachter] Koch und Ralph [Gutachter] Kennel. „Optimization of Velocity and Displacement Measurement with Optical Encoder and Laser Self-Mixing Interferometry / Hui Sun ; Gutachter: Alexander W. Koch, Ralph Kennel ; Betreuer: Ralph Kennel“. München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/1230552693/34.
Der volle Inhalt der QuelleAl-Hasani, Firas Ali Jawad. „Multiple Constant Multiplication Optimization Using Common Subexpression Elimination and Redundant Numbers“. Thesis, University of Canterbury. Electrical and Computer Engineering, 2014. http://hdl.handle.net/10092/9054.
Der volle Inhalt der QuelleNasrallah, Anthony. „Novel compression techniques for next-generation video coding“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT043.
Der volle Inhalt der QuelleVideo content now occupies about 82% of global internet traffic. This large percentage is due to the revolution in video content consumption. On the other hand, the market is increasingly demanding videos with higher resolutions and qualities. This causes a significant increase in the amount of data to be transmitted. Hence the need to develop video coding algorithms even more efficient than existing ones to limit the increase in the rate of data transmission and ensure a better quality of service. In addition, the impressive consumption of multimedia content in electronic products has an ecological impact. Therefore, finding a compromise between the complexity of algorithms and the efficiency of implementations is a new challenge. As a result, a collaborative team was created with the aim of developing a new video coding standard, Versatile Video Coding – VVC/H.266. Although VVC was able to achieve a more than 40% reduction in throughput compared to HEVC, this does not mean at all that there is no longer a need to further improve coding efficiency. In addition, VVC adds remarkable complexity compared to HEVC. This thesis responds to these problems by proposing three new encoding methods. The contributions of this research are divided into two main axes. The first axis is to propose and implement new compression tools in the new standard, capable of generating additional coding gains. Two methods have been proposed for this first axis. These two methods rely on the derivation of prediction information at the decoder side. This is because increasing encoder choices can improve the accuracy of predictions and yield less energy residue, leading to a reduction in bit rate. Nevertheless, more prediction modes involve more signaling to be sent into the binary stream to inform the decoder of the choices that have been made at the encoder. The gains mentioned above are therefore more than offset by the added signaling. If the prediction information has been derived from the decoder, the latter is no longer passive, but becomes active hence the concept of intelligent decoder. Thus, it will be useless to signal the information, hence a gain in signalization. Each of the two methods offers a different intelligent technique than the other to predict information at the decoder level. The first technique constructs a histogram of gradients to deduce different intra-prediction modes that can then be combined by means of prediction fusion, to obtain the final intra-prediction for a given block. This fusion property makes it possible to more accurately predict areas with complex textures, which, in conventional coding schemes, would rather require partitioning and/or finer transmission of high-energy residues. The second technique gives VVC the ability to switch between different interpolation filters of the inter prediction. The deduction of the optimal filter selected by the encoder is achieved through convolutional neural networks. The second axis, unlike the first, does not seek to add a contribution to the VVC algorithm. This axis rather aims to build an optimized use of the already existing algorithm. The ultimate goal is to find the best possible compromise between the compression efficiency delivered and the complexity imposed by VVC tools. Thus, an optimization system is designed to determine an effective technique for activating the new coding tools. The determination of these tools can be done either using artificial neural networks or without any artificial intelligence technique
Luo, Fangyi. „Post-Layout DFM optimization based on hybrid encoded topological layout /“. Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.
Der volle Inhalt der QuelleZhang, Yuanzhi. „Algorithms and Hardware Co-Design of HEVC Intra Encoders“. OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1769.
Der volle Inhalt der QuelleNguyen, Ngoc-Mai. „Stratégies d'optimisation de la consommation pour un système sur puce encodeur H.264“. Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT049/document.
Der volle Inhalt der QuellePower consumption for Systems-on-Chip induces strong constraints on their design. Power consumption affects the system reliability, cooling cost, and battery lifetime for Systems-on-Chips powered by battery. With the pace of semiconductor technology, power optimization has become a tremendous challenging issue together with Silicon area and/or performance optimization, especially for mobile applications. Video codec chips are used in various applications ranging for video conferencing, security and monitoring systems, but also entertainment applications. To meet the performance and power consumptions constraints encountered for mobile applications, video codecs are favorably preferred to be implemented in hardware rather than in software. This hardware implementation will lead to better power efficiency and real-time requirements. Nowadays, one of the most efficient standards for video applications is the H.264 Advanced Video Coding (H.264/AVC) which provides better video quality at a lower bit-rate than the previous standards. To bring the standard into commercial products, especially for hand-held devices, designers need to apply design approaches dedicated to low-power circuits. They also need to implement mechanisms to control the circuit power consumption. This PhD thesis is conducted in the framework of the VENGME H.264/AVC hardware encoder design. The platform is split in several modules and the VENGME Entropy Coder and bytestream Network Abstraction Layer data packer (EC-NAL) module has been designed during this PhD thesis, taking into account and combining several state-of-the-art solutions to minimise the power consumption. From simulation results, it has been seen that the EC-NAL module presents better power figures than the already published solutions. Then, the VENGME H.264 encoder architecture has been analyzed and power estimations at RTL level have been performed to extract the platform power figures. Then, from these power figures, it has been decided to implement power control on the EC-NAL module. This latter contains a FIFO whose level can be controlled via an appropriate scaling of the clock frequency on the NAL side, which leads to the implementation of a Dynamic Frequency Scaling (DFS) approach based on the control of the FIFO occupancy level. The control law has been implemented in hardware (full-custom) and the closed-loop system stability has been studied. Simulation results show the effectiveness of the proposed DVS strategy that should be extended to the whole H.264 encoder platform
Bücher zum Thema "Encoder optimization"
Caplan, Stephanie Tanya. Optimization of binary gabor zone plate encoded holography techniques with visible wavelengths. Birmingham: University of Birmingham, 1997.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Encoder optimization"
Shu, Ruo, Shibao Li und Xin Pan. „An Optimization Scheme for SVAC Audio Encoder“. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 221–29. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-662-44980-6_25.
Der volle Inhalt der QuelleQinglei, Meng, Yao Chunlian und Li Bo. „Video Encoder Optimization Implementation on Embedded Platform“. In Lecture Notes in Computer Science, 870–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881223_111.
Der volle Inhalt der QuelleZhang, Guanghao, Dongshun Cui, Shangbo Mao und Guang-Bin Huang. „Sparse Bayesian Learning for Extreme Learning Machine Auto-encoder“. In Proceedings in Adaptation, Learning and Optimization, 319–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23307-5_34.
Der volle Inhalt der QuelleKumar, Saurav, Satvik Gupta, Vishvender Singh, Mohit Khokhar und Prashant Singh Rana. „Parameter Optimization for H.265/HEVC Encoder Using NSGA II“. In Advances in Intelligent Systems and Computing, 105–18. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3325-4_11.
Der volle Inhalt der QuelleLiu, Hui, Hang-cheng Zeng und Bu Pu. „Implementation and Optimization of H.264 Encoder Based on TMS320DM6467“. In Lecture Notes in Electrical Engineering, 465–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-26001-8_61.
Der volle Inhalt der QuelleHsu, Chi-Yuan, und Antonio Ortega. „Joint Encoder and VBR Channel Optimization with Buffer and Leaky Bucket Constraints“. In Multimedia Communications and Video Coding, 409–17. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0403-6_50.
Der volle Inhalt der QuelleCao, Li, Xiaoyun Zhang und Zhiyong Gao. „An Efficient Optimization of Real-Time AVS+ Encoder in Low Bitrate Condition“. In Communications in Computer and Information Science, 265–75. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4211-9_26.
Der volle Inhalt der QuelleTran, Duc Hoa, Michel Meunier und Farida Cheriet. „Deep Image Clustering Using Self-learning Optimization in a Variational Auto-Encoder“. In Pattern Recognition. ICPR International Workshops and Challenges, 736–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68790-8_56.
Der volle Inhalt der QuelleBrahmane, A. V., und B. Chaitanya Krishna. „Chaotic Biogeography Based Optimization Using Deep Stacked Auto Encoder for Big Data Classification“. In Evolutionary Artificial Intelligence, 379–89. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8438-1_27.
Der volle Inhalt der QuelleMai, Zhi-Yi, Chun-Ling Yang, Lai-Man Po und Sheng-Li Xie. „A New Rate-Distortion Optimization Using Structural Information in H.264 I-Frame Encoder“. In Advanced Concepts for Intelligent Vision Systems, 435–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11558484_55.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Encoder optimization"
Gomes, Diullei M., und Isah A. Lawal. „Drainage Strategy Optimization Using Machine Learning Methods“. In SPE Nigeria Annual International Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/217092-ms.
Der volle Inhalt der QuelleLuis Bustamante, Alvaro, José M. Molina López und Miguel A. Patricio. „Video encoder optimization via evolutionary multiobjective optimization algorithms“. In the 11th Annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1569901.1570189.
Der volle Inhalt der QuelleHu, Qiang, Xiaoyun Zhang, Zhiyong Gao und Jun Sun. „Analysis and optimization of x265 encoder“. In 2014 Visual Communications and Image Processing (VCIP). IEEE, 2014. http://dx.doi.org/10.1109/vcip.2014.7051616.
Der volle Inhalt der QuelleMerkte, Philipp, Jordi Bayo Singla, Karsten Muller und Thomas Wiegand. „Stereo video encoder optimization for mobile applications“. In 2011 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON 2011). IEEE, 2011. http://dx.doi.org/10.1109/3dtv.2011.5877217.
Der volle Inhalt der QuelleJohnson, N., K. J. Mohan, K. E. Janson und J. Jose. „Optimization of incremental optical encoder pulse processing“. In 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s). IEEE, 2013. http://dx.doi.org/10.1109/imac4s.2013.6526510.
Der volle Inhalt der QuelleYangxia Xiang, Huimin Zhang, Xiaoxuan Xiang, Dazhi Chen und Ling Xiong. „Optimization of H.264 encoder based on SSE2“. In 2010 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2010. http://dx.doi.org/10.1109/pic.2010.5688015.
Der volle Inhalt der QuelleTsai, Chia-Ming, Yuwen He, Jie Dong, Yan Ye, Xiaoyu Xiu und Yong He. „Joint-layer encoder optimization for HEVC scalable extensions“. In SPIE Optical Engineering + Applications, herausgegeben von Andrew G. Tescher. SPIE, 2014. http://dx.doi.org/10.1117/12.2063470.
Der volle Inhalt der QuelleHu, Zhiqiang, Lun-hui Deng und RuiLv. „Cache optimization for real time MPEG-4 encoder“. In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM). IEEE, 2009. http://dx.doi.org/10.1109/cccm.2009.5267937.
Der volle Inhalt der QuelleQin, Han, Zeyu Jiang, Yonghua Wang, Hongwei Guo, Ce Zhu, Dandan Ding und Zoe Liu. „Temporally Dependent Rate-Distortion Optimization for AV1 Encoder“. In 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2022. http://dx.doi.org/10.1109/bmsb55706.2022.9828747.
Der volle Inhalt der QuelleWang, Ting-Feng, Yung-Jhe Yan, Hou-Chi Chiang, Tsan Lin Chen und Mang Ou-Yang. „Coding optimization for the absolute optical rotary encoder“. In 2018 International Automatic Control Conference (CACS). IEEE, 2018. http://dx.doi.org/10.1109/cacs.2018.8606741.
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