Machine learning is used to refine a probability distribution for entropy coding video or image data. A probability distribution is determined for symbols associated with a video block (e.g., quantized transform coefficients, such as during encoding, or syntax elements from a bitstream, such as ...
Fast Fractal Compression of Satellite and Medical Images Based on Domain-Range Entropy based on the domain-range entropy is proposed to reduce the encoding time, while maintaining the fidelity and compression ratio of the decoded image. The... VRP Vaddella,RB Inampudi - 《Journal of Applied Co...
Color Image CompressionWavelet TransformEntropy CodingSignMagnitudeIJCSIOne of the advantages of the Discrete Wavelet Transform (DWT) compared to Fourier Transform (e.g. Discrete Cosine Transform DCT) is its ability to provide both spatial and frequency localization of image energy. However, WT ...
doubling constant of a set should be at least as large as the doubling constant of the image of that set under a homomorphism, times the doubling constant of a typical fiber of that homomorphism; and furthermore, one should only be close to equality if the fibers “line up” in some ...
Sign in to download full-size image Fig. 1. Mechanism of entropy transfer for general system. The value of entropy generation cannot be negative, however the changes in entropy of the system may be positive, negative or zero. The entropy of an isolated system during an irreversible process al...
[29] achieve state-of-the-art results for image compression using INRs by then quantiz- ing these modulations and fine-tuning them to recover per- formance. They additionally introduce the use of L1 regu- larization, arguing that it approximately induces lower en- tropy in...
MLIC ++ : Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression [Arxiv] [OpenReview] is accepted at ICML 2023 Neural Compression Workshop ! Compared with version presented at Neural Compression Workshop, ICML 2023 at OpenReview, in the latest arxiv version, we add the...
We propose an end-to-end trainable image compression framework with a multi-scale and context-adaptive entropy model, especially for low bitrate compression. Due to the success of autoregressive priors in probabilistic generative model, the complementary combination of autoregressive and hierarchical priors...
Joint image compression and encryption based on compressed sensing and entropy coding. In Proceedings of the IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA), Batu Ferringhi, Malaysia, 10–12 March 2017. [Google Scholar] Ye, G.; Pan, C.; Huang, X.; Zhao, ...
3.2.7. Image and Signal Processing Nonadditive entropies have been profusely used in image and signal processing, in order to improve speed and clarity. Two such examples are here exhibited. The detection of possibly pathological microcalcifications as revealed in mammograms can be sensibly improved ...