Efficient multiplier-less parametric integer approximate transform based on 16-points DCT for image compression

Brahimi, Nabila; Bouden, Toufik; Brahimi, Tahar; Boubchir, Larbi

doi:10.1007/s11042-021-11348-3

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Because DCT and wavelet transformers are designed for piecewise stationary signals, they cannot preserve the quality of the edges and textures of the image [16]. Nowadays, Directional wavelet transforms [17] are developed to fix standard wavelet conversion problems and significantly improve JPEG 2000 compression performance. Encoding within the standard high-efficiency video coding (HEVC) [18] is also a method of image compression.…”

Section: Related Workmentioning

confidence: 99%

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Ghadami

Rahebi

2023

Soft Comput

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Image compression is one of the most attractive and practical topics. Without image compression, the image size becomes too large for storage. The transmission of uncompressing images on computer networks slows down and network bandwidth is wasted. Various approaches to image compression have been proposed so far, one of which is vector quantization using mathematical concepts and image processing to compress images. The LBG algorithm is a practical algorithm for compressing images using vector quantization concepts. Most researchers have used metaheuristic and optimization algorithms with a modeling approach of swarm behavior of living things to improve the quality of the LBG compression algorithm. This study uses a meta-heuristic method based on sine and cosine algorithms (SCA) to improve the quality of the image compression algorithm. In the proposed mathematical modeling method, the SCA algorithm is improved using spiral equations. The improved SCA algorithm is then used to find the optimal codebook in the LBG compression algorithm. Finding a better codebook in the proposed method will increase the quality of the compression images. The proposed method implemented in MATLAB software and experiments showed that the PSNR index in the proposed method improve the ratio of the LBG algorithm by about 13.73%. Evaluations show that the PSNR index of compressed images in the proposed method is higher and better than PBM, CS-LBG, FA-LBG, BA-LBG, HBMO-LBG, QPSO-LBG, PSO-LBG. The result shows the proposed method (or ISCA-LBG) has less time complexity than HHO and WOA compression algorithms.

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Section: Related Workmentioning

confidence: 99%

Compression of images with a mathematical approach based on sine and cosine equations and vector quantization (VQ)

Ghadami

Rahebi

2023

Soft Comput

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“…In [14], a new scheme was implemented to reduce the computational complexity of the DCT where only a few arithmetic addition operations are utilized in its implementation over 8 × 8 image blocks. To improve the quality of the transform, Brahimi et al [15] implemented DCT using a small set of arithmetic addition and multiplication operations. In both previous schemes [14,15], the quality of transform is influenced by the number of coefficients retained.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the quality of the transform, Brahimi et al [15] implemented DCT using a small set of arithmetic addition and multiplication operations. In both previous schemes [14,15], the quality of transform is influenced by the number of coefficients retained. DWT [16,17] applies low-and high-pass filters to decompose the image into multiple subimages in multiresolution representation.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Multichimera Transform for Color Image Representation

2022

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Mathematically representing an image with only a small number of coefficients has been attempted a few times. These attempts represent initial steps to achieve this goal and showed promising results by either working on a small image block size or utilizing a codebook built using a complex operation. The use of the codebook complicated the entire transformation process. In this work, we overcome these difficulties by developing a new scheme called systematic multichimera transform (SMCT). This transform employs simple mathematical functions called fractal half functions to independently build a codebook of image contents and size. These functions satisfy the symmetry under fractal form while breaking the orthogonality condition. The transform can deal with different image block sizes such as 8×8, 16×16, and 32×32. The encoding process is conducted by repetitively finding the similarity between image blocks and codebook blocks to achieve data reduction and preserve important information. The coefficients of the matching process are then employed in the decoding process to reconstruct the image. SMCT produced the highest structural similarity index (SSIM) and a competitive Peak Signal to Noise Ratio (PSNR) over the standard discrete wavelet transform (DWT) and discrete cosine transform (DCT) without degrading important image content.

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