[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 |