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Adaptive block size selection in a hybrid image compression algorithm employing the DCT and SVD

Garima Garg
Garima Garg
 und 
Raman Kumar
Raman Kumar
   | 14. Feb. 2024
International Journal on Smart Sensing and Intelligent Systems's Cover Image
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Article Category: Research article
Online veröffentlicht: 14. Feb. 2024
Seitenbereich: -
Eingereicht: 11. Okt. 2023
DOI: https://doi.org/10.2478/ijssis-2024-0005
Schlüsselwörter
© 2024 Garima Garg et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Image compression techniques.
Image compression techniques.

Figure 2:

Adopted methodology for image compression. DCT, discrete cosine transform; SVD, singular value decomposition.
Adopted methodology for image compression. DCT, discrete cosine transform; SVD, singular value decomposition.

Figure 3:

Working flow of the proposed hybrid algorithm. DCT, discrete cosine transform; SVD, singular value decomposition.
Working flow of the proposed hybrid algorithm. DCT, discrete cosine transform; SVD, singular value decomposition.

Figure 4:

Comparative analysis of compression ratios attained for several image datasets. DCT, discrete cosine transform; SVD, singular value decomposition
Comparative analysis of compression ratios attained for several image datasets. DCT, discrete cosine transform; SVD, singular value decomposition

Figure 5:

Comparative analysis of PSNR values attained. BSDS, Berkeley segmentation dataset; DCT, discrete cosine transform; PSNR, peak signal-to-noise ratio; SVD, singular value decomposition.
Comparative analysis of PSNR values attained. BSDS, Berkeley segmentation dataset; DCT, discrete cosine transform; PSNR, peak signal-to-noise ratio; SVD, singular value decomposition.

Figure 6:

Visual comparison of original and compressed images for different techniques. DCT, discrete cosine transform; SVD, singular value decomposition.
Visual comparison of original and compressed images for different techniques. DCT, discrete cosine transform; SVD, singular value decomposition.

Figure 7:

Comparative analysis of CIDs attained for several image datasets. DCT, discrete cosine transform; SVD, singular value decomposition.
Comparative analysis of CIDs attained for several image datasets. DCT, discrete cosine transform; SVD, singular value decomposition.

SSIM values obtained for several image datasets using the DCT-SVD hybrid technique

Dataset SSIM
Kodak Lossless True Color Image Suite 0.93
Lena image 0.94
BSDS 0.89
ImageNet 0.92

Compression ratios attained for several image datasets using the DCT-SVD hybrid technique

Dataset Compression ratio
Kodak Lossless True Color Image Suite 58.34
Lena image 63.12
BSDS 55.76
ImageNet 57.89

CIDs obtained for several image datasets using the DCT-SVD hybrid technique

Dataset CID
Kodak lossless true color image suite 0.86
Lena image 0.82
BSDS 0.89
ImageNet 0.87

Summary of recent work on image compression

Ref. No. Dataset used Adopted methodology Techniques used Advantages Disadvantages Solutions
[18] Kodak dataset Adaptive block size selection and DCT-SVD hybrid DCT, SVD, and adaptive processing High compression and good quality Complexity in hybridization Adaptive hybridization
[19] UCID dataset Wavelet transform Wavelet transform Multiresolution representation Limited to certain images Improved wavelet selection
[20] CALTECH dataset Huffman coding Huffman coding No quality loss Limited compression ratio Enhanced entropy coding
[21] ImageNet dataset DCT-based compression Discrete cosine transform Established standard Lossy compression Improved quantization
[22] Custom dataset Iterated function system Fractal encoding Good compression Iteration limits Adaptive fractal generation
[23] MNIST dataset DCT-DWT hybrid DCT and DWT Multifrequency representation High computational cost Improved parallel processing
[24] COCO dataset Singular value decomposition Singular value decomposition Noise robustness Singular value truncation Adaptive truncation threshold
[25] CIFAR-10 dataset Neural network-based approach Neural networks Adaptive learning Training complexity Improved model architecture
[26] ImageNet dataset Contextual analysis Contextual processing Improved quality Complexity Efficient context modeling
[27] Medical images Adaptive block size selection and transform coding DCT and Huffman coding Lossless compression Limited to medical images Improved coding strategies
[28] Custom dataset Vector quantization Vector quantization High compression ratios Information loss Enhanced vector codebooks
[29] COCO dataset Adaptive processing based on content DCT and adaptive strategies Improved quality and efficient compression Complexity in content analysis Enhanced adaptive strategies
[30] ImageNet dataset Pyramid-based compression Pyramid transform Multiresolution representation Complexity Optimized pyramid levels
[31] Kodak dataset Progressive compression approach DCT and SVD Stepwise quality enhancement Progressive transmission complexity Improved transmission order
[32] CALTECH dataset Block-based processing and Huffman coding Block processing and Huffman coding Balanced quality compression Block artifacts Enhanced block processing
[33] ImageNet dataset Simultaneous compression and encryption DCT and encryption techniques Secure compression Increased complexity Improved encryption algorithms
[34] Custom dataset Arithmetic coding Arithmetic coding High compression and lossless compression Complexity Enhanced probability modeling
[35] CIFAR-10 dataset DCT–neural network hybrid DCT and neural networks Adaptive compression and improved quality Training complexity Enhanced training strategies
[36] COCO dataset Wavelet transform Wavelet transform Multifrequency representation Complexity Enhanced transform selection
[37] Custom dataset Contextual Huffman coding Contextual analysis and Huffman coding Improved compression Complexity Enhanced context modeling
[38] ImageNet dataset Multiresolution encoding Discrete wavelet transform Progressive quality and multiresolution Complexity Adaptive wavelet selection

PSNR values attained for several image datasets using the DCT-SVD hybrid technique

Dataset PSNR (dB)
Kodak Lossless True Color Image Suite 38.21
Lena image 39.08
BSDS 36.75
ImageNet 37.52

Dataset used for experimentation

Dataset name Number of images Image types Resolution Content complexity
Kodak Lossless True Color Image Suite 24 Natural sceneries Varied Moderate
Lena image 1 Portrait 512 × 512 Moderate
BSDS 200 Natural sceneries Varied High
ImageNet 1000 Various Varied High
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