Fast Linde–Buzo–Gray (FLBG) Algorithm for Image Compression through Rescaling Using Bilinear Interpolation

Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde–Buzo–Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Par...

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Veröffentlicht in:Journal of imaging Jg. 10; H. 5; S. 124
Hauptverfasser: Bilal, Muhammmad, Ullah, Zahid, Mujahid, Omer, Fouzder, Tama
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.05.2024
Schlagworte:
Algorithms
bat algorithm
Block codes
Clustering
codebook
Coders
Codes
Comparative analysis
Complexity
Compression ratio
computational time
Computing time
firefly algorithm
Fourier transforms
Heuristic methods
Image coding
Image compression
Image quality
Interpolation
Linde–Buzo–Gray
Medical imaging
Methods
Optimization techniques
Particle swarm optimization
Quality standards
Rescaling
Search algorithms
Swarm intelligence
Pakistan
Linde–Buzo–Gray
bat algorithm
peak signal to noise ratio
image compression
vector quantization
firefly algorithm
codebook
computational time
ISSN:2313-433X, 2313-433X
Online-Zugang:Volltext
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Zusammenfassung:Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde–Buzo–Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Particle Swarm Optimization (PSO), the Cuckoo search algorithm (CS), bat algorithm, and firefly algorithm (FA), are used for codebook design. These algorithms are primarily focused on improving the image quality in terms of the PSNR and SSIM but use exhaustive searching to find the optimum codebook, which causes the computational time to be very high. In our study, our algorithm enhances LBG by minimizing the computational complexity by reducing the total number of comparisons among the codebook and training vectors using a match function. The input image is taken as a training vector at the encoder side, which is initialized with the random selection of the vectors from the input image. Rescaling using bilinear interpolation through the nearest neighborhood method is performed to reduce the comparison of the codebook with the training vector. The compressed image is first downsized by the encoder, which is then upscaled at the decoder side during decompression. Based on the results, it is demonstrated that the proposed method reduces the computational complexity by 50.2% compared to LBG and above 97% compared to the other LBG-based algorithms. Moreover, a 20% reduction in the memory size is also obtained, with no significant loss in the image quality compared to the LBG algorithm.
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ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging10050124