Advertisement

Research on multimedia play mode and image optimization based on compensation factor adaptive model

  • Yuling LiuEmail author
Article
  • 10 Downloads

Abstract

When traditional multimedia network video image is compressed and transmitted to compensate, because of the different loss of video image features in the acquisition process, the error of compressed transmission compensation is large and the efficiency is low. Firstly, the NLMS algorithm and the improved NLMS algorithm are analyzed. To solve the problem that the compensation factor in the algorithm is too large due to the severe network shake, the NLMS algorithm is further improved by adaptively adjusting the compensation factor coefficient with the change of the network and the prediction error. The research shows that the multimedia playback mode and the image optimization system software structure based on the compensation factor adaptive model realize the dynamic adaptation of the system to various network conditions and the image optimization function during video playback by adopting the bandwidth adaptive strategy and method.. The conclusion shows that in the multimedia network video image compression transmission, the improved compensation method has higher performance in multimedia network video image optimization and real-time compression transmission compensation, which has certain advantages compared with the traditional compensation method.

Keywords

Compensation factor self-adaptation Multimedia playback Image optimization 

Notes

Funding

Chongqing College of Electronic Engineering fund project“Research on Constructing KAQ High-level Talents Training Model in Higher Vocational Education Based on Outstanding Talents Training Plan“.(XJSK201806).

References

  1. 1.
    Aggarwal CC, Wolf JL (2015) Yu P S. Optimization Issues in Multimedia Systems. Int J Intell Syst 13(12):1113–1135CrossRefGoogle Scholar
  2. 2.
    Chen Z, Huang W, Lv Z (2015) Towards a face recognition method based on uncorrelated discriminant sparse preserving projection. Multimed Tools Appl 76(17):1–15Google Scholar
  3. 3.
    Chen J, Su KX, Wang WX et al (2014) Residual distributed compressive video sensing based on double side information. Acta Automat Sin 40(10):2316–2323CrossRefGoogle Scholar
  4. 4.
    Dahmane A, Larabi S, Bilasco IM et al (2015) Head pose estimation based on face symmetry analysis. SIViP 9(8):1871–1880CrossRefGoogle Scholar
  5. 5.
    Dang CT, Radha H (2014) Heterogeneity Image Patch Index and Its Application to Consumer Video Summarization. IEEE Trans Image Process 23(6):2704–2718MathSciNetCrossRefGoogle Scholar
  6. 6.
    De Faria JWV, Teixeira MJ, de Moura Sousa Júnior L, et al (2016) Virtual and stereoscopic anatomy: when virtual reality meets medical education. J Neurosurg 1–7Google Scholar
  7. 7.
    Domański M, Stankiewicz O, Wegner K, Kurc M, Konieczny J, Siast J, Stankowski J, Ratajczak R, Grajek T (2013) High efficiency 3D video coding using new tools based on view synthesis. IEEE Trans Image Process 22(9):3517–3527Google Scholar
  8. 8.
    Geldhof GJ, Gestsdottir S, Stefansson K et al (2015) Selection, optimization, and compensation: The structure, reliability, and validity of forced-choice versus Likert-type measures in a sample of late adolescents. Int J Behav Dev 39(2):171–185CrossRefGoogle Scholar
  9. 9.
    Kordelas GA, Alexiadis DS, Daras P et al (2016) Content-Based Guided Image Filtering, Weighted Semi-Global Optimization, and Efficient Disparity Refinement for Fast and Accurate Disparity Estimation. IEEE Transactions on Multimedia 18(2):155–170CrossRefGoogle Scholar
  10. 10.
    Kuo Y (2014) Yang, et al. An efficient mode decision algorithm for H.264/AVC intra prediction. Multimed Tools Appl 72(2):1803–1821CrossRefGoogle Scholar
  11. 11.
    Lee A, Jun DS, Choi JS (2015) Fast motion estimation using priority-based inter-prediction mode decision method in high efficiency video coding. J Real-Time Image Proc 12(2):1–9Google Scholar
  12. 12.
    Li W, Ren P (2015) 2015 IEEE International Conference on Multimedia Big Data (BigMM) - Beijing, China (2015.4.20–2015.4.22). 2015 IEEE International Conference on Multimedia Big Data - Fast CABAC Rate Estimation for HEVC Mode Decision, pp. 171–175Google Scholar
  13. 13.
    Li Y, Yang G, Zhu Y et al (2015) Adaptive mode decision for multiview video coding based on macroblock position constraint model. J Real-Time Image Proc 12(3):1–8Google Scholar
  14. 14.
    Li X, Zhao H, Huang H et al (2016) Interactive image recoloring by combining global and local optimization. Multimed Tools Appl 75(11):6431–6443CrossRefGoogle Scholar
  15. 15.
    Lin L, Wang X, Yang W et al (2015) Discriminatively Trained And-Or Graph Models for Object Shape Detection. IEEE Trans Pattern Anal Mach Intell 37(5):959–972CrossRefGoogle Scholar
  16. 16.
    Madi A, Ziou D (2014) Color constancy for visual compensation of projector displayed image. Displays 35(1):6–17CrossRefGoogle Scholar
  17. 17.
    Rui L, Maolin C, Yan S et al (2016) Pipeline Bending Strain Measurement and Compensation Technology Based on Wavelet Neural Network. Journal of Sensors 2016:1–7CrossRefGoogle Scholar
  18. 18.
    Schuwerk C, Xu X, Chaudhari R et al (2015) Compensating the Effect of Communication Delay in Client-Server--Based Shared Haptic Virtual Environments. ACM Transactions on Applied Perception 13(1):1–22CrossRefGoogle Scholar
  19. 19.
    Sullivan GJ, Ohm JR, Han WJ et al (2013) Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Transactions on Circuits and Systems for Video Technology 22(12):1649–1668CrossRefGoogle Scholar
  20. 20.
    Sun YC, Tsai WJ (2014) Rate-distortion optimized mode selection method for multiple description video coding. Multimed Tools Appl 72(2):1411–1439CrossRefGoogle Scholar
  21. 21.
    Yang J, Zhang X, Peng W et al (2015) A novel regularized K-SVD dictionary learning based medical image super-resolution algorithm. Multimed Tools Appl 75(21):1–14Google Scholar
  22. 22.
    Yi C, Cai J (2015) Multi-Item Spectrum Auction for Recall-Based Cognitive Radio Networks With Multiple Heterogeneous Secondary Users. Vehicular Technology IEEE Transactions 64(2):781–792CrossRefGoogle Scholar
  23. 23.
    Zhang Q, Izquierdo E (2013) Multifeature analysis and semantic context learning for image classification. ACM Trans Multimed Comput Commun Appl 9(2):1–20CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Digital MediaChongqing College of Electronic EngineeringChongqingChina

Personalised recommendations