Abstract
This paper plans to develop a novel image compression model with four major phases. (i) Segmentation (ii) Feature Extraction (iii) ROI classification (iv) Compression. The image is segmented into two regions by Adaptive ACM. The result of ACM is the production of two regions, this model enables separate ROI classification phase. For performing this, the features corresponding to GLCM are extracted from the segmented parts. Further, they are subjected to classification via NN, in which new training algorithm is adopted. As a main novelty JA and WOA are merged together to form J-WOA with the aim of tuning the ACM (weighting factor and maximum iteration), and training algorithm of NN, where the weights are optimized. This model is referred as J-WOA-NN. This classification model exactly classifies the ROI regions. During the compression process, the ROI regions are handled by JPEG-LS algorithm and the non-ROI region are handled by wavelet-based lossy compression algorithm. Finally, the decompression model is carried out by adopting the same reverse process.
Highlights
Over the decades, the demand for multimedia products attaints faster growth in the field of communication
6.1 Experimental Setup The proposed J-WOA-NN image compression technique is carried out in MATLAB, and the results obtained for analysis
To the of the implementation, the proposed J-WOA-NN image compression model is compared with the existing image compression models like LM-NN (Ngia & Sjoberg, 2000), FF-NN (Lv, et al, 2019), GWO-NN (Kohli & Arora, 2018), WOA-NN (Mirjalili & Lewis, 2016) and JA-NN (Rao, 2016) in terms of like MEP, SMAPE, MASE, MAE, RMSE, one-Norm, two- Norm and infinity –Norm
Summary
The demand for multimedia products attaints faster growth in the field of communication. The digital images make the bandwidth insufficient as well as consume huge storage space of the memory devices (Miaou, et al, 2009; Lin & Hao, 2005). There is a necessity to diminish the data redundancy in the image with the desire of saving the hardware space and the transmission bandwidth. The redundancy may be due to the frequent repetition of the pixels across the image. The redundancies in the image are distinguished into, psycho-visual redundancy spatial redundancy, and coding redundancy. The elimination of the correlation among the pixels in the natural image via transform coding or predictive coding is referred to as Inter-pixel Redundancy or spatial redundancy. The psycho-visual redundancy is done to reduce the quantity of data to make the visual information
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