3,156 research outputs found
ICA based algorithms for computing optimal 1-D linear block transforms in variable high-rate source coding
International audienceThe Karhunen-Loève Transform (KLT) is optimal for transform coding of Gaussian sources, however, it is not optimal, in general, for non-Gaussian sources. Furthermore, under the high-resolution quantization hypothesis, nearly everything is known about the performance of a transform coding system with entropy constrained scalar quantization and mean-square distortion. It is then straightforward to find a criterion that, when minimized, gives the optimal linear transform under the abovementioned conditions. However, the optimal transform computation is generally considered as a difficult task and the Gaussian assumption is then used in order to simplify the calculus. In this paper, we present the abovementioned criterion as a contrast of independent component analysis modified by an additional term which is a penalty to non-orthogonality. Then we adapt the icainf algorithm by Pham in order to compute the transform minimizing the criterion either with no constraint or with the orthogonality constraint. Finally, experimental results show that the transforms we introduced can (1) outperform the KLT on synthetic signals, (2) achieve slightly better PSNR for high-rates and better visual quality (preservation of lines and contours) for medium-to-low rates than the KLT and 2-D DCT on grayscale natural images
An Introduction to Neural Data Compression
Neural compression is the application of neural networks and other machine
learning methods to data compression. Recent advances in statistical machine
learning have opened up new possibilities for data compression, allowing
compression algorithms to be learned end-to-end from data using powerful
generative models such as normalizing flows, variational autoencoders,
diffusion probabilistic models, and generative adversarial networks. The
present article aims to introduce this field of research to a broader machine
learning audience by reviewing the necessary background in information theory
(e.g., entropy coding, rate-distortion theory) and computer vision (e.g., image
quality assessment, perceptual metrics), and providing a curated guide through
the essential ideas and methods in the literature thus far
- …