Figure 1:
Image compression techniques.
Table 1:
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 |
[i] DCT, discrete cosine transform; DWT, discrete wavelet transform; SVD, singular value decomposition.
Table 2:
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 |
[i] BSDS, Berkeley segmentation dataset.
Figure 2:
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.
Figure 4:
Comparative analysis of compression ratios attained for several image datasets. DCT, discrete cosine transform; SVD, singular value decomposition
Table 3:
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 |
[i] BSDS, Berkeley segmentation dataset; DCT, discrete cosine transform; SVD, singular value decomposition.
Table 4.
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 |
[i] BSDS, Berkeley segmentation dataset; DCT, discrete cosine transform; PSNR, peak signal-to-noise ratio; 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.
Table 5.
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 |
[i] BSDS, Berkeley segmentation dataset; DCT, discrete cosine transform; SSIM, structural similarity index; SVD, singular value decomposition.
Figure 6:
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.