Performance Analysis of Compression Techniques for Multimedia Data

Budhewar, Anupama S.; Doye, Dharmpal D.

doi:10.1007/978-3-030-03131-2_12

Cited by 4 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method requires more time, and computation complexity is more for large frame videos. Budhewar et al [2] represented HSI based method to distinguish foreground and background. HSI based method recognizes the foreground and background efficiently.…”

Section: Literature Surveymentioning

confidence: 99%

SVM Based Temporal Compression Techniques for Video Compression

Budhewar¹,

Doye²

2020

Bio-Inspired Neurocomputing

Self Cite

View full text Add to dashboard Cite

Compression is an important role in multimedia communication. In this paper, the problems of compression, quality degradation, best matched video, and image are discussed. Block matching algorithms compress the image block by block, and it also operates in a temporal manner. Proposed method is a SVM based algorithm compression. It is the combination of intensity and motion estimation. The proposed technique's compression gains of 92% accuracy with respective to static JPEG-LS, as it is applied in a frame basis. It achieves a good computation complexity and reduces it. An experimental result shows the better performance than existing methods. The method requires less best match points compared to existing methods. The quality of compression is improved when compared to the previous methods. Keywords Support vector machine (SVM) • Support vector decomposition (SVD) • Hue saturation intensity (HSI) • Region of interest (ROI) • Probability error (PE)

show abstract

Section: Literature Surveymentioning

confidence: 99%