What are the advantages and challenges of using multiresolution analysis for image compression in EE?

2 Advantages of MRA for image compression

One of the main advantages of MRA for image compression is that it allows for a flexible and adaptive representation of the image, depending on the application and the desired quality. By using wavelets, MRA can capture both the global and local features of the image, and adjust the resolution level according to the complexity of the signal. For example, regions with smooth variations can be represented by fewer coefficients at a lower resolution, while regions with sharp changes can be represented by more coefficients at a higher resolution. This way, MRA can achieve a high compression ratio without sacrificing too much visual quality.

3 Challenges of MRA for image compression

However, MRA also faces some challenges for image compression, especially in terms of computational complexity and storage requirements. MRA involves multiple operations, such as filtering, downsampling, upsampling, and reconstruction, which can be costly in terms of time and memory. Moreover, MRA requires a large number of coefficients to represent the subbands, which can increase the storage space and the transmission bandwidth. Therefore, MRA needs to be combined with other techniques, such as quantization, coding, and thresholding, to reduce the number of coefficients and to eliminate the noise and redundancy in the subbands.

4 Examples of MRA for image compression

There are many examples of MRA for image compression in electrical engineering, ranging from standard formats to specialized applications. One of the most widely used formats is JPEG 2000, which is based on the discrete wavelet transform (DWT), a type of MRA that uses orthogonal wavelets. JPEG 2000 can achieve a high compression performance and a low distortion, and can also support features such as progressive decoding, region of interest coding, and lossless compression. Another example is SPIHT (set partitioning in hierarchical trees), which is an algorithm that uses a modified DWT and a tree structure to encode the wavelet coefficients. SPIHT can achieve a high bit rate and a low error rate, and can also adapt to different image characteristics.

5 Future trends of MRA for image compression

MRA is still an active area of research and development for image compression in electrical engineering, with many potential directions for improvement and innovation. For example, using non-orthogonal wavelets, such as biorthogonal wavelets or contourlets, can better capture the directional and geometric features of the image. Multivariate wavelets, such as tensor wavelets or vector wavelets, can exploit the correlation between different color channels or modalities of the image. Adaptive wavelets, such as lifting wavelets or wavelet packets, can customize the wavelet basis according to the image content or user preference. Additionally, deep learning techniques like convolutional neural networks or generative adversarial networks can learn the optimal wavelet representation or compression scheme from a large dataset of images.