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Quantum computing has emerged as a transformative paradigm, with revolutionary potential in numerous fields, including quantum image processing and compression. Applications that depend on large scale image data could benefit greatly from parallelism and quantum entanglement, which would allow images to be encoded and decoded with unprecedented efficiency and data reduction capability. This paper provides a comprehensive overview of the rapidly evolving field of quantum image compression, including its foundational principles, methods, challenges, and potential uses. The paper will also feature a thorough exploration of the fundamental concepts of quantum qubits as image pixels, quantum gates as image transformation tools, quantum image representation, as well as basic quantum compression operations. Our survey shows that work is still sparse on the practical implementation of quantum image compression algorithms on physical quantum computers. Thus, further research is needed in order to attain the full advantage and potential of quantum image compression algorithms on large-scale fault-tolerant quantum computers. [ABSTRACT FROM AUTHOR]

We propose a novel computation control method for variable-complexity Fano decoders with buffers. Our method substantially lowers the rates of data block loss associated with conventional Fano decoders. For reasonably large buffer sizes, our method outperforms Layland's buffer management scheme with block loss rates close to the theoretical lower bound. Index Terms--Buffers, computational complexity, convolutional codes, Fano decoder.

Thelow-complexity RAHT (Region-Adaptive Hierarchical Transform) and its corresponding Dyadic decomposition have been incorporated in the MPEG (Moving Picture Expert Group) standardization for encoding the attribute values in 3D point clouds. While significant gains were observed with the replacement of RAHT by Dyadic RAHT, the standard scheme uses only one type of decomposition for the entire point cloud. In this letter, we propose a novel adaptive scheme of switching between RAHT and Dyadic RAHT to achieve early termination of Dyadic RAHT by using 3D gradient filters. Improvements were made on the state-of-the-art scheme that uses only Dyadic RAHT transformation. The proposed method was tested on the publicly available 3D point cloud datasets. The results shows we can achieve cumulative compression gain of up to 11% over all-dyadic approach, by avoiding further decomposition in some cases. [ABSTRACT FROM AUTHOR]

To achieve efficient lossless compression of hyperspectral images, we design a concatenated neural network, which is capable of extracting both spatial and spectral correlations for accurate pixel value prediction. Unlike conventional neural network based methods in the literature, the proposed neural network functions as an adaptive filter, thereby eliminating the need for pre-training using decompressed data. To meet the demand for low-complexity onboard processing, we use a shallow network with only two hidden layers for efficient feature extraction and predictive filtering. Extensive simulations on commonly used hyperspectral datasets and the standard CCSDS test datasets show that the proposed approach attains significant improvements over several other state-of-the-art methods, including standard compressors such as ESA, CCSDS-122, and CCSDS-123. [ABSTRACT FROM AUTHOR]

Margin is a significant algorithmic parameter that has an impact on the performance of margin-based machine learning algorithms. Margin setting algorithm (MSA) is a novel margin-based learning algorithm. However, there is no comprehensive study concerning the impact of setting margin on enhancing the performance of MSA. In this article, we studied the impact of margin on performances of MSA by comparing it to another popular margin-based algorithm, the support vector machine (SVM). This comparison comprehensively analyzes and compares how margin affects training performance and generalization, both theoretically and experimentally. In our theoretical analysis, margin definition and margin impacts are comprehensively discussed by demonstrating how they affect the decision boundary. Experimental analysis is performed on two-dimensional Gaussian data sets and benchmark data sets. The experimental results support our theoretical analysis, revealing that with an increased margin, training performance gets worse and generalization tends to improve within a certain range. c 2018 Society for Imaging Science and Technology. [ABSTRACT FROM AUTHOR]