The effect of using minimum decreasing technique on enhancing the quality of lossy compressed images

Otair, Mohammed; Hasan, Osama Abdulraziq; Abualigah, Laith

doi:10.1007/s11042-022-13404-y

Cited by 1 publication

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meta-heuristic optimization algorithms are built around two fundamental tasks: (1) exploration, which refers to the algorithm's capacity to examine non-local (global) locations. (2) Exploitation: this refers to the algorithm's capacity to discover superior optimization solutions within those overall solutions. A suitable balance between these tactics is required (exploration and exploitation) [42].…”

Section: Related Workmentioning

confidence: 99%

“…Each image with a size of (256 × 256). Five thresholds (2)(3)(4)(5)(6) were used in this research's multi-level image segmentation procedure. Optimization systems using swarm intelligence usually display random behavior.…”

Section: Image Datasetmentioning

confidence: 99%

“…The segmentation system is becoming more favored and has been used in domains such as bio-medical, urban vehicles, farming production, etc. Image segmentation is required when component selection or tracking image details is needed [2]. In the meantime, the rate of succeeding image processing stages, such as recognition, is involved in the image segmentation outcome [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

et al. 2023

Self Cite

View full text Add to dashboard Cite

This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

show abstract

Section: Related Workmentioning

confidence: 99%