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Journal articles on the topic 'Coding framework'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Shaban, Amirreza, Hamid R. Rabiee, Mahyar Najibi, and Safoora Yousefi. "From Local Similarities to Global Coding: A Framework for Coding Applications." IEEE Transactions on Image Processing 24, no. 12 (December 2015): 5074–85. http://dx.doi.org/10.1109/tip.2015.2465171.

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2

Tanaka, Midori, Tomoyuki Takanashi, and Takahiko Horiuchi. "Glossiness-aware Image Coding in JPEG Framework." Journal of Imaging Science and Technology 64, no. 5 (September 1, 2020): 50409–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.5.050409.

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Abstract In images, the representation of glossiness, translucency, and roughness of material objects (Shitsukan) is essential for realistic image reproduction. To date, image coding has been developed considering various indices of the quality of the encoded image, for example, the peak signal-to-noise ratio. Consequently, image coding methods that preserve subjective impressions of qualities such as Shitsukan have not been studied. In this study, the authors focus on the property of glossiness and propose a method of glossiness-aware image coding. Their purpose is to develop an encoding algorithm that produces images that can be decoded by standard JPEG decoders, which are commonly used worldwide. The proposed method consists of three procedures: block classification, glossiness enhancement, and non-glossiness information reduction. In block classification, the types of glossiness in a target image are classified using block units. In glossiness enhancement, the glossiness in each type of block is emphasized to reduce the amount of degradation of glossiness during JPEG encoding. The third procedure, non-glossiness information reduction, further compresses the information while maintaining the glossiness by reducing the information in each block that does not represent the glossiness in the image. To test the effectiveness of the proposed method, the authors conducted a subjective evaluation experiment using paired comparison of images coded by the proposed method and JPEG images with the same data size. The glossiness was found to be better preserved in images coded by the proposed method than in the JPEG images.

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3

Barth, Erhardt, and Andrew B. Watson. "A geometric framework for nonlinear visual coding." Optics Express 7, no. 4 (August 14, 2000): 155. http://dx.doi.org/10.1364/oe.7.000155.

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4

Li, Weiping. "Video coding using a vector wavelet framework." Journal of Electronic Imaging 7, no. 3 (July 1, 1998): 558. http://dx.doi.org/10.1117/1.482597.

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5

Tan, Min, Raymond W. Yeung, Siu-Ting Ho, and Ning Cai. "A Unified Framework for Linear Network Coding." IEEE Transactions on Information Theory 57, no. 1 (January 2011): 416–23. http://dx.doi.org/10.1109/tit.2010.2090216.

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6

Zhu, Weijia, Wenpeng Ding, Jizheng Xu, Yunhui Shi, and Baocai Yin. "Screen Content Coding Based on HEVC Framework." IEEE Transactions on Multimedia 16, no. 5 (August 2014): 1316–26. http://dx.doi.org/10.1109/tmm.2014.2315782.

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7

Halloush, Mohammed, and Hayder Radha. "Network Coding with Multi-Generation Mixing: A Generalized Framework for Practical Network Coding." IEEE Transactions on Wireless Communications 10, no. 2 (February 2011): 466–73. http://dx.doi.org/10.1109/twc.2011.120810.090280.

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8

Li, Jianjun, and Esam Abdel-Raheem. "Modeling DV/DVCPRO Standards on Reconfigurable Video Coding Framework." Journal of Electrical and Computer Engineering 2010 (2010): 1–7. http://dx.doi.org/10.1155/2010/509394.

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After more than 20 years, several video coding standards and technologies have been delivered. Less consideration is taken on their commonalities and interoperations. Specification and reference code of case by case is time consuming. The MPEG reconfigurable video coding (RVC) framework is a new standard under development by MPEG. It aims to provide a unified high-level specification of current MPEG video coding technologies. In this framework, the decoder is built as a configuration of video coding tools taken from MPEG toolbox library. Up to now, MPEG-4 simple profile and China audio video coding standard (AVS) decoders have been successfully modeled with RVC framework. In this paper, we examine another video standard, that is, DV/DVCPRO, and model it with RVC-CAL. The flexibility and ease of RVC-CAL is demonstrated as well as the validation of RVC modeling.

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9

Pavlovic, V., P. Moulin, and K. Ramchandran. "An integrated framework for adaptive subband image coding." IEEE Transactions on Signal Processing 47, no. 4 (April 1999): 1024–38. http://dx.doi.org/10.1109/78.752600.

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10

Barajas-Solano, Crisostomo, Juan-Marcos Ramirez, and Henry Arguello. "Convolutional sparse coding framework for compressive spectral imaging." Journal of Visual Communication and Image Representation 66 (January 2020): 102690. http://dx.doi.org/10.1016/j.jvcir.2019.102690.

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11

Burnod, Y., Pierre Baraduc, A. Battaglia-Mayer, Emmanuel Guigon, Etienne Koechlin, S. Ferraina, F. Lacquaniti, and R. Caminiti. "Parieto-frontal coding of reaching: an integrated framework." Experimental Brain Research 129, no. 3 (November 15, 1999): 0325–46. http://dx.doi.org/10.1007/s002210050902.

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12

Torres-Boza, Diana, Meshia Cédric Oveneke, Fengna Wang, Dongmei Jiang, Werner Verhelst, and Hichem Sahli. "Hierarchical sparse coding framework for speech emotion recognition." Speech Communication 99 (May 2018): 80–89. http://dx.doi.org/10.1016/j.specom.2018.01.006.

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13

Bhattacharyya, Shuvra S., Johan Eker, Jörn W. Janneck, Christophe Lucarz, Marco Mattavelli, and Mickaël Raulet. "Overview of the MPEG Reconfigurable Video Coding Framework." Journal of Signal Processing Systems 63, no. 2 (July 25, 2009): 251–63. http://dx.doi.org/10.1007/s11265-009-0399-3.

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14

Ramaswamy, V. N., K. R. Namuduri, and N. Ranganathan. "Context-based lossless image coding using EZW framework." IEEE Transactions on Circuits and Systems for Video Technology 11, no. 4 (April 2001): 554–59. http://dx.doi.org/10.1109/76.915361.

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15

Samčović, Andreja. "Analysis of Coding Gain and Optimal Bit Allocation in Motion-Compensated Video Compression." Journal of Electrical Engineering 63, no. 2 (March 1, 2012): 129–32. http://dx.doi.org/10.2478/v10187-012-0020-z.

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Analysis of Coding Gain and Optimal Bit Allocation in Motion-Compensated Video CompressionThis paper describes mathematical frameworks on temporal and spatial predictive processing in the motion-compensated video compression. Firstly, the coding gain over intra coding is derived, regarding the bit allocation algorithm and Lagrange multiplier method. The optimal ordering of three different picture types (I, P and B pictures) is clarified according to image source characteristics. Secondly, a novel framework with the block-based multihypothesis motion-compensated optimal coding gain and bit allocation are derived in a closed-form expression.

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Tsang, Sik-Ho, Yui-Lam Chan, and Wei Kuang. "Standard compliant light field lenslet image coding model using enhanced screen content coding framework." Journal of Electronic Imaging 28, no. 05 (October 23, 2019): 1. http://dx.doi.org/10.1117/1.jei.28.5.053027.

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17

Zouidi, Naima, Amina Kessentini, Wassim Hamidouche, Nouri Masmoudi, and Daniel Menard. "Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding." Electronics 11, no. 23 (December 2, 2022): 4001. http://dx.doi.org/10.3390/electronics11234001.

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In mid-2020, the new international video coding standard, namely versatile video coding (VVC), was officially released by the Joint Video Expert Team (JVET). As its name indicates, the VVC enables a higher level of versatility with better compression performance compared to its predecessor, high-efficiency video coding (HEVC). VVC introduces several new coding tools like multiple reference lines (MRL) and matrix-weighted intra-prediction (MIP), along with several improvements on the block-based hybrid video coding scheme such as quatree with nested multi-type tree (QTMT) and finer-granularity intra-prediction modes (IPMs). Because finding the best encoding decisions is usually preceded by optimizing the rate distortion (RD) cost, introducing new coding tools or enhancing existing ones requires additional computations. In fact, the VVC is 31 times more complex than the HEVC. Therefore, this paper aims to reduce the computational complexity of the VVC. It establishes a large database for intra-prediction and proposes a multitask learning (MTL)-based intra-mode decision framework. Experimental results show that our proposal enables up to 30% of complexity reduction while slightly increasing the Bjontegaard bit rate (BD-BR).

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Xambó, Anna, Alexander Lerch, and Jason Freeman. "Music Information Retrieval in Live Coding: A Theoretical Framework." Computer Music Journal 42, no. 4 (May 2019): 9–25. http://dx.doi.org/10.1162/comj_a_00484.

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Music information retrieval (MIR) has a great potential in musical live coding because it can help the musician–programmer to make musical decisions based on audio content analysis and explore new sonorities by means of MIR techniques. The use of real-time MIR techniques can be computationally demanding and thus they have been rarely used in live coding; when they have been used, it has been with a focus on low-level feature extraction. This article surveys and discusses the potential of MIR applied to live coding at a higher musical level. We propose a conceptual framework of three categories: (1) audio repurposing, (2) audio rewiring, and (3) audio remixing. We explored the three categories in live performance through an application programming interface library written in SuperCollider, MIRLC. We found that it is still a technical challenge to use high-level features in real time, yet using rhythmic and tonal properties (midlevel features) in combination with text-based information (e.g., tags) helps to achieve a closer perceptual level centered on pitch and rhythm when using MIR in live coding. We discuss challenges and future directions of utilizing MIR approaches in the computer music field.

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19

Bao, Xingkai, and Jing Tiffany Li. "Generalized Adaptive Network Coded Cooperation (GANCC): A Unified Framework for Network Coding and Channel Coding." IEEE Transactions on Communications 59, no. 11 (November 2011): 2934–38. http://dx.doi.org/10.1109/tcomm.2011.091411.070335.

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20

Reyes, Alex D. "Mathematical framework for place coding in the auditory system." PLOS Computational Biology 17, no. 8 (August 2, 2021): e1009251. http://dx.doi.org/10.1371/journal.pcbi.1009251.

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In the auditory system, tonotopy is postulated to be the substrate for a place code, where sound frequency is encoded by the location of the neurons that fire during the stimulus. Though conceptually simple, the computations that allow for the representation of intensity and complex sounds are poorly understood. Here, a mathematical framework is developed in order to define clearly the conditions that support a place code. To accommodate both frequency and intensity information, the neural network is described as a space with elements that represent individual neurons and clusters of neurons. A mapping is then constructed from acoustic space to neural space so that frequency and intensity are encoded, respectively, by the location and size of the clusters. Algebraic operations -addition and multiplication- are derived to elucidate the rules for representing, assembling, and modulating multi-frequency sound in networks. The resulting outcomes of these operations are consistent with network simulations as well as with electrophysiological and psychophysical data. The analyses show how both frequency and intensity can be encoded with a purely place code, without the need for rate or temporal coding schemes. The algebraic operations are used to describe loudness summation and suggest a mechanism for the critical band. The mathematical approach complements experimental and computational approaches and provides a foundation for interpreting data and constructing models.

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21

Yin, Hoover H. F., Bin Tang, Ka Hei Ng, Shenghao Yang, Xishi Wang, and Qiaoqiao Zhou. "A Unified Adaptive Recoding Framework for Batched Network Coding." IEEE Journal on Selected Areas in Information Theory 2, no. 4 (December 2021): 1150–64. http://dx.doi.org/10.1109/jsait.2021.3126634.

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22

Bortolussi, Luca, Liviu P. Dinu, Laura Franzoi, and Andrea Sgarro. "Coding Theory: A General Framework and Two Inverse Problems." Fundamenta Informaticae 141, no. 4 (December 9, 2015): 297–310. http://dx.doi.org/10.3233/fi-2015-1277.

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23

WANG, Minghui, Xun HE, Xin JIN, and Satoshi GOTO. "Framework of a Contour Based Depth Map Coding Method." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E95.A, no. 8 (2012): 1270–79. http://dx.doi.org/10.1587/transfun.e95.a.1270.

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24

Jingning Han, Vinay Melkote, and Kenneth Rose. "An Estimation-Theoretic Framework for Spatially Scalable Video Coding." IEEE Transactions on Image Processing 23, no. 8 (August 2014): 3684–97. http://dx.doi.org/10.1109/tip.2014.2331761.

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25

Zheng, Yayu, Fan Zhou, Xiang Tian, and Yaowu Chen. "Lightweight content-adaptive coding in joint analyzing-encoding framework." IEEE Transactions on Consumer Electronics 54, no. 2 (May 2008): 614–22. http://dx.doi.org/10.1109/tce.2008.4560138.

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26

Yu, Qi-Yue, Hsiao-Hwa Chen, and Wei-Xiao Meng. "A Unified Multiuser Coding Framework for Multiple-Access Technologies." IEEE Systems Journal 13, no. 4 (December 2019): 3781–92. http://dx.doi.org/10.1109/jsyst.2019.2924342.

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27

Ramirez, Ignacio, and Guillermo Sapiro. "An MDL Framework for Sparse Coding and Dictionary Learning." IEEE Transactions on Signal Processing 60, no. 6 (June 2012): 2913–27. http://dx.doi.org/10.1109/tsp.2012.2187203.

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28

Benazza-Benyahia, Amel, and Jean-Christophe Pesquet. "A unifying framework for lossless and progressive image coding." Pattern Recognition 35, no. 3 (March 2002): 627–38. http://dx.doi.org/10.1016/s0031-3203(01)00065-6.

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29

Siriwongpairat, W. P., Weifeng Su, M. Olfat, and K. J. Ray Liu. "Multiband-OFDM MIMO coding framework for UWB communication systems." IEEE Transactions on Signal Processing 54, no. 1 (January 2006): 214–24. http://dx.doi.org/10.1109/tsp.2005.861092.

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30

Ryu, Seungchul, Jungdong Seo, Jin Young Lee, and Kwanghoon Sohn. "Advanced motion vector coding framework for multiview video sequences." Multimedia Tools and Applications 67, no. 1 (November 18, 2011): 49–70. http://dx.doi.org/10.1007/s11042-011-0930-y.

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31

Sridhara, S. R., and N. R. Shanbhag. "Coding for system-on-chip networks: a unified framework." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 13, no. 6 (June 2005): 655–67. http://dx.doi.org/10.1109/tvlsi.2005.848816.

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32

Zhihai He and S. K. Mitra. "A unified rate-distortion analysis framework for transform coding." IEEE Transactions on Circuits and Systems for Video Technology 11, no. 12 (2001): 1221–36. http://dx.doi.org/10.1109/76.974677.

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33

Kamal, Sarwar, Nilanjan Dey, Sonia Farhana Nimmy, Shamim H. Ripon, Nawab Yousuf Ali, Amira S. Ashour, Wahiba Ben Abdessalem Karaa, Gia Nhu Nguyen, and Fuqian Shi. "Evolutionary framework for coding area selection from cancer data." Neural Computing and Applications 29, no. 4 (August 2, 2016): 1015–37. http://dx.doi.org/10.1007/s00521-016-2513-3.

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34

Prynne, C. J., A. A. Paul, B. Dibba, and L. M. A. Jarjou. "Gambian Food Records: a New Framework for Computer Coding." Journal of Food Composition and Analysis 15, no. 4 (August 2002): 349–57. http://dx.doi.org/10.1006/jfca.2002.1074.

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35

Liao, Honghong, Jinhai Xiang, Weiping Sun, and Shengsheng Yu. "Adaptive Aggregating Multiresolution Feature Coding for Image Classification." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/847608.

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The Bag of Visual Words (BoW) model is one of the most popular and effective image classification frameworks in the recent literature. The optimal formation of a visual vocabulary remains unclear, and the size of the vocabulary also affects the performance of image classification. Empirically, larger vocabulary leads to higher classification accuracy. However, larger vocabulary needs more memory and intensive computational resources. In this paper, we propose a multiresolution feature coding (MFC) framework via aggregating feature codings obtained from a set of small visual vocabularies with different sizes, where each vocabulary is obtained by a clustering algorithm, and different clustering algorithm discovers different aspect of image features. In MFC, feature codings from different visual vocabularies are aggregated adaptively by a modified Online Passive-Aggressive Algorithm under the histogram intersection kernel, which lead to a closed-form solution. Experiments demonstrate that the proposed method (1) obtains the same if not higher classification accuracy than the BoW model with a large visual vocabulary; and (2) needs much less memory and computational resources.

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36

Masuda, Naoki, and Kazuyuki Aihara. "Duality of Rate Coding and Temporal Coding in Multilayered Feedforward Networks." Neural Computation 15, no. 1 (January 1, 2003): 103–25. http://dx.doi.org/10.1162/089976603321043711.

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A functional role for precise spike timing has been proposed as an alternative hypothesis to rate coding. We show in this article that both the synchronous firing code and the population rate code can be used dually in a common framework of a single neural network model. Furthermore, these two coding mechanisms are bridged continuously by several modulatable model parameters, including shared connectivity, feedback strength, membrane leak rate, and neuron heterogeneity. The rates of change of these parameters are closely related to the response time and the timescale of learning.

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37

Anandaraj, M., P. Ganeshkumar, K. P. Vijayakumar, and K. Selvaraj. "An Efficient Framework for Large Scale Multimedia Content Distribution in P2P Network: I2NC." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/303505.

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Network coding (NC) makes content distribution more effective and easier in P2P content distribution network and reduces the burden of the original seeder. It generalizes traditional network routing by allowing the intermediate nodes to generate new coded packet by combining the received packets. The randomization introduced by network coding makes all packets equally important and resolves the problem of locating the rarest block. Further, it reduces traffic in the network. In this paper, we analyze the performance of traditional network coding in P2P content distribution network by using a mathematical model and it is proved that traffic reduction has not been fully achieved in P2P network using traditional network coding. It happens due to the redundant transmission of noninnovative information block among the peers in the network. Hence, we propose a new framework, called I2NC (intelligent-peer selection and incremental-network coding), to eliminate the unnecessary flooding of noninnovative coded packets and thereby to improve the performance of network coding in P2P content distribution further. A comparative study and analysis of the proposed system is made through various related implementations and the results show that 10–15% of traffic reduced and improved the average and maximum download time by reducing original seeder’s workload.

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Chen, Yuxin, Christopher D. Andrews, Cindy E. Hmelo-Silver, and Cynthia D'Angelo. "Coding schemes as lenses on collaborative learning." Information and Learning Sciences 121, no. 1/2 (December 12, 2019): 1–18. http://dx.doi.org/10.1108/ils-08-2019-0079.

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Purpose Computer-supported collaborative learning (CSCL) is widely used in different levels of education across disciplines and domains. Researchers in the field have proposed various conceptual frameworks toward a comprehensive understanding of CSCL. However, as the definition of CSCL is varied and contextualized, it is critical to develop a shared understanding of collaboration and common definitions for the metrics that are used. The purpose of this research is to present a synthesis that focuses explicitly on the types and features of coding schemes that are used as analytic tools for CSCL. Design/methodology/approach This research collected coding schemes from researchers with diverse backgrounds who participated in a series of workshops on collaborative learning and adaptive support in CSCL, as well as coding schemes from recent volumes of the International Journal of Computer-Supported Collaborative learning (ijCSCL). Each original coding scheme was reviewed to generate an empirically grounded framework that reflects collaborative learning models. Findings The analysis generated 13 categories, which were further classified into three domains: cognitive, social and integrated. Most coding schemes contained categories in the cognitive and integrated domains. Practical implications This synthesized coding scheme could be used as a toolkit for researchers to pay attention to the multiple and complex dimensions of collaborative learning and for developing a shared language of collaborative learning. Originality/value By analyzing a set of coding schemes, the authors highlight what CSCL researchers find important by making these implicit understandings of collaborative learning visible and by proposing a common language for researchers across disciplines to communicate by referencing a synthesized framework.

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39

Nath, Aritro, Paul Geeleher, and R. Stephanie Huang. "Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes." Briefings in Bioinformatics 21, no. 2 (January 17, 2019): 637–48. http://dx.doi.org/10.1093/bib/bby129.

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Abstract Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

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Tan-a-ram, Surapol, Anuchit Leelayuttho, Somphong Kittipiyakul, Wuthipong Pornsukjantra, Thundluck Sereevoravitgul, Apichart Intarapanich, Saowaluck Kaewkamnerd, and Dusadee Treeumnuk. "KidBright: An Open-Source Embedded Programming Platform with a Dedicated Software Framework in Support of Ecosystems for Learning to Code." Sustainability 14, no. 21 (November 4, 2022): 14528. http://dx.doi.org/10.3390/su142114528.

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The concept of coding at school has enabled educators and parents around the globe to become interested in coding as, these days, coding is regarded as a gateway to computational thinking for children. However, coding education in Thailand appears to lag behind that of many other countries due to the lack of accessible coding learning resources suitable for students as well as the limited number of teachers with coding experience. Regarding these issues, we have developed an open-source hardware-based coding platform named KidBright, based on these requirements: 1. making coding simple for novice learners through the use of graphical blocks with Thai and English support; 2. inspiring young students to develop creative embedded applications with minimal effort; and 3. providing sustainable support for coding education. KidBright is proposed as a coding learning tool that can motivate children to learn to code and develop embedded system projects using its block-based coding environment, the KidBright IDE, in conjunction with its embedded device, the KidBright board. KidBright is distinguished from other embedded programming platforms due to the deployment of a dedicated software framework as the backend of the KidBright IDE. In this article, we introduce KidBright and present the design, architecture, and demonstrations of the software framework, the key roles of which are to conceal low-level hardware issues from learners and to enable makers to develop new command blocks and hardware peripherals in support of KidBright. We claim that, with this particular design, KidBright can help support coding education sustainably. In particular, we present how a small research team introduces coding to a large number of learners who have little or no coding experience, resulting in an impact on coding education in the country.

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41

Müller, Ralf Reiner, Bernhard Martin Wilhelm Gäde, and Ali Bereyhi. "Linear Computation Coding: A Framework for Joint Quantization and Computing." Algorithms 15, no. 7 (July 20, 2022): 253. http://dx.doi.org/10.3390/a15070253.

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Here we introduce the new concept of computation coding. Similar to how rate-distortion theory is concerned with the lossy compression of data, computation coding deals with the lossy computation of functions. Particularizing to linear functions, we present an algorithmic approach to reduce the computational cost of multiplying a constant matrix with a variable vector, which requires neither a matrix nor vector having any particular structure or statistical properties. The algorithm decomposes the constant matrix into the product of codebook and wiring matrices whose entries are either zero or signed integer powers of two. For a typical application like the implementation of a deep neural network, the proposed algorithm reduces the number of required addition units several times. To achieve the accuracy of 16-bit signed integer arithmetic for 4k-vectors, no multipliers and only 1.5 adders per matrix entry are needed.

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42

Zeng, Bing, and Jingjing Fu. "Directional Discrete Cosine Transforms—A New Framework for Image Coding." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 3 (March 2008): 305–13. http://dx.doi.org/10.1109/tcsvt.2008.918455.

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43

Da Silva, Renam C., Fernando Pereira, and Eduardo A. B. Da Silva. "Toward Visualization and Searching: A Dual-Purpose Video Coding Framework." IEEE Access 8 (2020): 14860–74. http://dx.doi.org/10.1109/access.2020.2966692.

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44

Zhang, Kai, Yi-Wen Chen, Li Zhang, Wei-Jung Chien, and Marta Karczewicz. "An Improved Framework of Affine Motion Compensation in Video Coding." IEEE Transactions on Image Processing 28, no. 3 (March 2019): 1456–69. http://dx.doi.org/10.1109/tip.2018.2877355.

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45

Sehgal, A., A. Jagmohan, and N. Ahuja. "Wyner–Ziv Coding of Video: An Error-Resilient Compression Framework." IEEE Transactions on Multimedia 6, no. 2 (April 2004): 249–58. http://dx.doi.org/10.1109/tmm.2003.822995.

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46

Elyasi, Mehran, and Soheil Mohajer. "Determinant Coding: A Novel Framework for Exact-Repair Regenerating Codes." IEEE Transactions on Information Theory 62, no. 12 (December 2016): 6683–97. http://dx.doi.org/10.1109/tit.2016.2616137.

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47

Brown, Sharon A., Sandra L. Upchurch, and Gayle J. Acton. "A Framework for Developing a Coding Scheme for Meta-Analysis." Western Journal of Nursing Research 25, no. 2 (March 2003): 205–22. http://dx.doi.org/10.1177/0193945902250038.

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Sanz-Rodriguez, Sergio, and Fernando Diaz-de-Maria. "In-Layer Multibuffer Framework for Rate-Controlled Scalable Video Coding." IEEE Transactions on Circuits and Systems for Video Technology 22, no. 8 (August 2012): 1199–212. http://dx.doi.org/10.1109/tcsvt.2012.2198089.

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Xue, Yuanyi, and Yao Wang. "A Novel Video Coding Framework Using a Self-Adaptive Dictionary." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 12 (December 2018): 3478–91. http://dx.doi.org/10.1109/tcsvt.2017.2760143.

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Arildsen, T., M. N. Murthi, S. V. Andersen, and S. H. Jensen. "On Predictive Coding for Erasure Channels Using a Kalman Framework." IEEE Transactions on Signal Processing 57, no. 11 (November 2009): 4456–66. http://dx.doi.org/10.1109/tsp.2009.2025796.

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