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Stream/Area:
-
Subject:
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Paper ID:
IJSREM37806
Volume:
8 |
10 |
Oct 2024
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
ECE
Vidarbha Institute of Technology, Nagpur.
ECE
Vidarbha Institute of Technology, Nagpur.
The objective of this paper is to develop a low bit rate encoding for VQ problems such as real-time image coding. The encoding process applied is independent of the vector dimensions and does not perform any arithmetic operations. The decision tree is generated by an offline process.. A new systolic architecture to realize the encoder of full-search vector quantization (VQ) for high-speed applications is presented here Whenever we consider an image into fractal image, it has great importance and application in image processing field. Over past decades digital video compression technologies have become an integral part. Therefore the purpose is to improve image quality in Remote cardiac pulse measurement using Adaptive filter. It describes the approach to be used for feature extraction from many images.. This paper presents a real time application of compression of the image processing technique which can be efficiently used for the interfacing with any hardware. Therefore we have used Raspberry Pi in compression of image. We have developed an algorithm that is based on the endoscopic images that consist of the differential pulse code modulation. The compressors consist of a low cost YEF colour space converters and variable length predictive algorithm for lossless compression.
KEYWORDS
Edge detection is an important pre-processing step in image analysis. Edge detection is an important work for object recognition and is also an essential pre-processing step in image segmentation. These edge detection operators can have better edge effect under the circumstances of obvious edge and low noise. There are various edge detection methods in the domain of image edge detection, each having certain disadvantages. In this project, a new algorithm for the very low bit rate video coding system is presented. This algorithm contain Vector Quantization is used for lossy data compression, lossy data correction and density estimation, and coding parts. it also measure video compression ratio with specified extracting number of frame which have been selected by input video with image size by generating text matrix in VHDL.
1. N. S. Jayant and P. Noll, Digital Coding of Waveforms: Principles and Applications, Prentice Hall, 1984. 2. Classic reference for PCM, ADPCM, and quantization-based encoding. 3. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, Springer, 1992. 4. Important reference for scalar and vector quantization methods. 5. R. M. Gray,“Quantization in Task-Driven Sensing and Communication Systems,” IEEE Transactions on Information Theory. 6. ITU-T G.726 Standard Adaptive Differential Pulse Code Modulation (ADPCM) for low bit rate speech coding. 7. ITU-T G.729 Standard CS-ACELP based low bit rate speech encoder operating at 8 kbps.
Publication Date
5/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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