An enhanced video compression approach through RLAH encoding and KDENN algorithms

Recently, video transmission is going through many failures because of the limited size of the top-notch technique for storing large volume videos. Thus, video compression (VC) techniques are introduced, which try to eradicate various sorts of redundancies within or betwixt video sequences. However,...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:EURASIP journal on advances in signal processing Ročník 2024; číslo 1; s. 15 - 19
Hlavní autoři: Manjunatha, D. V., Dattathreya, Khan, Umair, Siddesh, G. K., Prabhakar, S. V., Sreenivasa, B. R., Muhammad, Taseer, Hassan, Ahmed M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.12.2024
Springer
Springer Nature B.V
SpringerOpen
Témata:
Algorithms
Block motion
Coding
Compression ratio
Data compression
Engineering
Frames (data processing)
Kalman filters
Kernel-based deep Elman neural network (KDENN)
Methods
Neural networks
Quantum Information Technology
Run length-based ASCII Huffman (RLAH) encoding
Signal,Image and Speech Processing
Spintronics
Venture capital
Video compression
Video transmission
Video compression
Compression ratio
Block motion
Run length-based ASCII Huffman (RLAH) encoding
Kernel-based deep Elman neural network (KDENN)
ISSN:1687-6180, 1687-6172, 1687-6180
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Recently, video transmission is going through many failures because of the limited size of the top-notch technique for storing large volume videos. Thus, video compression (VC) techniques are introduced, which try to eradicate various sorts of redundancies within or betwixt video sequences. However, the VC often falls short to maintain a good quality of compression if motion discontinuities are present in the video frames (VF). To trounce this challenge, this paper proposes an enhanced VC approach via run length-based ASCII Huffman (RLAH) encoding, Kernel-based deep Elman neural network (KDENN), together with modified Kalman filters (MKF) algorithms. Initially, the video is transmuted into frames, and the frame's color space model (CSM) is changed as of RGB to YC b C r . Next, the frames are bifurcated into [8 × 8] blocks, and the significant features are extracted as of every block. On account of these features, the KDENN identifies the motion of every block. Those blocks directly undergo a compression process in case of a single motion. Otherwise, MFK smoothens those blocks in order to eradicate the jitters and undesired movements, and then, it goes through compression. After that, RLAH encoding compresses the VF. Then, on the other side, the RLAH decoding algorithm decomposes the video. The results exhibit that the proposed work renders good quality videos with high PSNR value and also it trounces the prevailing compression techniques concerning compression ratio (CR).
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-024-01109-3