DCT-based color image compression algorithm using adaptive block scanning

Messaoudi, Abdelhamid; Benchabane, Fateh; Srairi, K.

doi:10.1007/s11760-019-01492-7

Cited by 20 publications

(10 citation statements)

References 25 publications

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“…The non-zero values and the chosen index vector are coded and stored. The proposed system shows better performance compared to several techniques including JPEG standard [7].…”

Section: Literature Surveymentioning

confidence: 94%

Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

Ibrahim

George

Hassan

2020

eijs

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In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the “ACDSee Ultimate 2020” software to evaluate the performance of the proposed system.

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“…The non-zero values and the chosen index vector are coded and stored. The proposed system shows better performance compared to several techniques including JPEG standard [7].…”

Section: Literature Surveymentioning

confidence: 94%

Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

Ibrahim

George

Hassan

2020

eijs

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“…After conversion to the YCbCr model, the image is divided into N × N non-overlapping blocks. For each block, the block color strength, the block edge strength, and the block texture strength are extracted using Walsh-Hadamard basis [16] vectors as given in (10), (12), and (13), respectively. The complete feature content of the block is generated by considering weighted combination of individual feature strengths using (15), known as block feature strength (BFS) of the block.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Block color strength (C sb ): It is defined as the amount of brightness in the image block which can be calculated as given in (10):…”

Section: Feature Extraction and Generation Of Block Feature Strength ...mentioning

confidence: 99%

Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization

et al. 2022

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To meet the high bit rate requirements in many multimedia applications, a lossy image compression algorithm based on Walsh–Hadamard kernel-based feature extraction, discrete cosine transform (DCT), and bi-level quantization is proposed in this paper. The selection of the quantization matrix of the block is made based on a weighted combination of the block feature strength (BFS) of the block extracted by projecting the selected Walsh–Hadamard basis kernels on an image block. The BFS is compared with an automatically generated threshold for applying the specific quantization matrix for compression. In this paper, higher BFS blocks are processed via DCT and high Q matrix, and blocks with lower feature strength are processed via DCT and low Q matrix. So, blocks with higher feature strength are less compressed and vice versa. The proposed algorithm is compared to different DCT and block truncation coding (BTC)-based approaches based on the quality parameters, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at constant bits per pixel (bpp). The proposed method shows significant improvements in performance over standard JPEG and recent approaches at lower bpp. It achieved an average PSNR of 35.61 dB and an average SSIM of 0.90 at a bpp of 0.5 and better perceptual quality with lower visual artifacts.

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“…Discrete Cosine Transform (DCT) as stated by Messaoudi et al [4]; Brahimi et al [5] and Discrete Wavelet Transform (DWT) as asserted by Boucetta and Melkemi [6]; Parkale, Nalbalwar [7] The two predominantly used approaches for the lossless compression of the image are used to divide the image into multiple sub-regions. The process of image quantization is being performed to convert the image into the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Secured Compression for 2D Medical Images Through the Manifold and Fuzzy Trapezoidal Correlation Function

Srinivasu

Norwawi

AMİRİPALLİ

et al. 2022

Gazi University Journal of Science

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DCT-based color image compression algorithm using adaptive block scanning

Cited by 20 publications

References 25 publications

Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization

Secured Compression for 2D Medical Images Through the Manifold and Fuzzy Trapezoidal Correlation Function

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