Answer:
For an application focused on compressing storage space of images and utilizing a CPU with a built-in Floating Point Unit (FPU) that can handle hardware-accelerated floating-point operations, the best-suited transform from the options provided would be:
b) DCT (Discrete Cosine Transform)
Explanation:
- DCT is widely used in image compression, particularly in JPEG compression. It effectively separates the image into different frequency components, allowing for the quantization of less important frequencies, which leads to significant compression.
- The DCT is particularly efficient for images because it tends to concentrate energy in a small number of coefficients, making it easier to discard less significant data without a substantial loss in quality.
- The FPU's capabilities in handling floating-point arithmetic are beneficial for the calculations involved in the DCT.
Other Options:
- a) Fast Walsh/Hadamard Transform: While it can be used for certain types of data, it is less common for image compression compared to DCT.
- c) FFT (Fast Fourier Transform): This is more suited for signal processing and is not typically used for image compression in the same way DCT is.
- d) KL Transform (Karhunen-Loève Transform): This is a statistical transform that can be used for data compression but is more complex and less commonly implemented in standard image compression algorithms compared to DCT.
In summary, the Discrete Cosine Transform (DCT) is the most appropriate choice for image compression in this context.