Low-complexity Rounded KLT Approximation for Image Compression
This addresses a bottleneck for real-time image compression applications, though it is an incremental improvement over existing KLT approximations.
The paper tackles the high computational complexity of the Karhunen-Loève transform (KLT) for real-time image compression by proposing low-complexity transforms derived by rounding KLT matrix elements, which perform well in compression experiments with low implementation cost.
The Karhunen-Loève transform (KLT) is often used for data decorrelation and dimensionality reduction. Because its computation depends on the matrix of covariances of the input signal, the use of the KLT in real-time applications is severely constrained by the difficulty in developing fast algorithms to implement it. In this context, this paper proposes a new class of low-complexity transforms that are obtained through the application of the round function to the elements of the KLT matrix. The proposed transforms are evaluated considering figures of merit that measure the coding power and distance of the proposed approximations to the exact KLT and are also explored in image compression experiments. Fast algorithms are introduced for the proposed approximate transforms. It was shown that the proposed transforms perform well in image compression and require a low implementation cost.