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RETRACTED ARTICLE: Development of video compression using EWNS linear transformation and un-repetition simulated contrary based resurgence procedure

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This article was retracted on 20 September 2022

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Abstract

In the recent years, video transmission area endures several failures owing to the limited amount of a cutting edge technique to store large sized videos. For this reason, video compression method is used. For compression of videos, frame formation can be done by splitting the video frames by A_Frames, B_Frames and C_Frames. These frames will stay unchanged entire process; it will also utilize the memory for computational purpose. The proposed work consists of 2 phases. In the first phase, Acclimatize Frame Formation (AFF) is used to expand the characteristic of video coding and EWNS linear transformation (ELT) is initiated for substituting B_Frames with neither A_Frame nor C_Frame. In the second phase, un-repetition simulated contrary based resurgence procedure (URSCRP) is proposed for restoration of videos that demonstrates tiny convolution and time necessity together with conservation of restoration feature. Simulation results shows that URSCRP provides better accuracy and optimization compared to other related procedures. URSCRP provides PSNR of 30 dB, accuracy of 98% and minimized amount of elapsed time compared to related procedures.

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References

  1. Aghamaleki JA, Behrad A (2016) Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding. Signal Process Image Commun 47:289–302

    Article  Google Scholar 

  2. Ahmadi A, Pouladi F, Salehinejad H, Talebi S (2011) Fast two-stage global motion estimation: a blocks and pixels sampling approach. Intelligent interactive multimedia systems and services 11:143–151

    Article  Google Scholar 

  3. Candes E (2008) The restricted isometry property and its implications. Journal Comptes Rendus Mathematique 346(9–10):589–592

    Article  MathSciNet  Google Scholar 

  4. Donoho DL, Tsaig Y, Drori I, Starck J-L (2012) Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit (StOMP). IEEE Trans Inf Theory 58(2):1094–1121

    Article  Google Scholar 

  5. Drori I (2008) Compressed video sensing. In BMVA symposium on 3D video—Analysis, display, and applications

  6. Fazel M, Candes E, Recht B, Parrilo P (2007). Compressed sensing and robust recovery of low rank matrices. In 42nd Asilomar conference on signals, systems and computers (pp. 1043–1047). October 26–29, 2008

  7. Kumar V, Sharma KG, Jalal AS (2013).Macro-block mode decision in MPEG-2 video compression using machine learning. Lecture Notes in Electrical Engineering. pp. 149–158

  8. Liu J, Qiao F, Wei Q, Yang H (2013) A novel video compression method based on underdetermined blind source separation. Multimedia and Ubiquitous Engineering, Lecture Notes in Electrical Engineering 240:13–20

    Article  Google Scholar 

  9. Masiero R, Quer G, Rossi M, Zorzi M (2009). A Bayesian analysis of compressive sensing data recovery in wireless sensor networks. In Proceedings of international conference on ultra-modern telecommunications & workshops (pp. 1–6). October 12–14, 2009

  10. Mukherjee R, Debattista K, Bashford-Rogers T, Vangorp P, Mantiuk R, Bessa M, Waterfield B, Chalmers A (2016) Objective and subjective evaluation of high dynamic range video compression. Signal Process Image Commun 47:426–437

    Article  Google Scholar 

  11. Needell D, Tropp JA (2009) CoSaMP: iterative signal recovery from incomplete and inaccurate sample. Journal of Applied and Computational Analysis 26(3):301–321

    MathSciNet  MATH  Google Scholar 

  12. Patel VM, Chellappa R (2013) Sparse representation and compressive sensing for imaging and vision. Springer, Berlin

    Book  Google Scholar 

  13. Paul M, Lin W, Lau CT, Lee BS (2013) Video coding with dynamic background, EURASIP. Journal on Advances in Signal Processing 11:1–17

    Google Scholar 

  14. Rajalakshmi K, Mahesh K (2016) A review on video compression and embedding techniques. International Journal of Computer Applications (0975–8887) 141(12):32–36

    Article  Google Scholar 

  15. Tony CT, Wang L (2011) Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans Inf Theory 57(7):4680–4688

    Article  MathSciNet  Google Scholar 

  16. Worasucheep C (2015) A Hybrid Artificial Bee Colony with Differential Evolution, International Journal of Machine Learning and Computing. 5

  17. Xu D, Wang R (2016) Two-dimensional reversible data hiding-based approach for intra-frame error concealment in H.264/AVC. Signal processing: image communication. Vol 47:289–302

    Google Scholar 

  18. Yang J, Li WT, Shi XW, Xin L, Yu JF (2013) A hybrid ABC-DE algorithm and its application for time-modulated arrays pattern synthesis. IEEE Trans Antennas Propag 61(11):5485–5495. https://doi.org/10.1109/TAP.2013.2279093

  19. Zatt B, Porto M, Scharcanski J (2010). Gop structure adaptive to the video content for efficient H.264/AVC encoding. Proceedings of 17th International conference in image processing. pp. 3053–3056

  20. Zhi-xue L, Gang L, Hao Z, Xi-qin W (2012). Sparse-driven SAR imaging using MMV-StOMP. In Proceedings of 1st international workshop on compressed sensing applied to radar. IEEE Press, May 14–16

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Correspondence to R. Karthik Ganesh.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11042-022-13971-0

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Karthik Ganesh, R., Kanthavel, R. & Dhaya, R. RETRACTED ARTICLE: Development of video compression using EWNS linear transformation and un-repetition simulated contrary based resurgence procedure. Multimed Tools Appl 79, 3519–3541 (2020). https://doi.org/10.1007/s11042-018-6008-3

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  • DOI: https://doi.org/10.1007/s11042-018-6008-3

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