Quantum Approach to Satellite Image Data Compression and Analysis

Due to the rapid development of satellite imaging sensors, high-resolution images are being generated for use. Various image processing algorithms, such as deep learning models, require images of reduced sizes given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for any deep learning model. This paper proposes a novel approach to compress satellite images using quantum computing. A comparative study on different standard data embedding techniques used in quantum computing is undertaken. We propose four quantum compression techniques (𝑄𝐶𝑇s) by extending the unitary operations of amplitude encoding for compressing satellite images. The proposed methods provide exponential scaling as amplitude encoding is used, where 2𝑛 classical data values are encoded into 𝑛 qubits. Compression performance, visual evaluation, and quality metric evaluation were carried out to assess the proposed compression techniques. Our experimental results showed that the crucial patterns in images are retained in the compressed images without quality loss even after 75% compression. The compressed images can be used for post-processing tasks such as classification using classical or quantum computing algorithms.Due to the rapid development of satellite imaging sensors, high-resolution images are being generated for use. Various image processing algorithms, such as deep learning models, require images of reduced sizes given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for any deep learning model. This paper proposes a novel approach to compress satellite images using quantum computing. A comparative study on different standard data embedding techniques used in quantum computing is undertaken. We propose four quantum compression techniques (𝑄𝐶𝑇s) by extending the unitary operations of amplitude encoding for compressing satellite images. The proposed methods provide exponential scaling as amplitude encoding is used, where 2𝑛 classical data values are encoded into 𝑛 qubits. Compression performance, visual evaluation, and quality metric evaluation were carried out to assess the proposed compression techniques. Our experimental results showed that the crucial patterns in images are retained in the compressed images without quality loss even after 75% compression. The compressed images can be used for post-processing tasks such as classification using classical or quantum computing algorithms.

Nov 2, 20221 citation

Quantum Approach to Image Data Encoding and Compression

Sathwik Reddy Majji, Avinash Chalumuri, B. S. B. S. Manoj

High-resolution images are being generated due to the rapid development of image sensors. Various image processing algorithms require images of reduced sizes, given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for many applications. This letter proposes a novel approach to compress images using quantum computing. A comparative study on different standard data encoding techniques used in quantum computing is undertaken. We propose four quantum compression techniques by extending the unitary operations of amplitude embedding for compressing images. The proposed methods provide exponential scaling as amplitude embedding is used, where $2^{n}$ classical data values are encoded into $n$ qubits. Compression performance, visual evaluation, and objective evaluation are carried out to assess the proposed compression techniques. Our experimental results show that the crucial patterns in images are retained in the compressed images even at 75% compression. The compressed images can be used for postprocessing tasks using classical or quantum computing algorithms.High-resolution images are being generated due to the rapid development of image sensors. Various image processing algorithms require images of reduced sizes, given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for many applications. This letter proposes a novel approach to compress images using quantum computing. A comparative study on different standard data encoding techniques used in quantum computing is undertaken. We propose four quantum compression techniques by extending the unitary operations of amplitude embedding for compressing images. The proposed methods provide exponential scaling as amplitude embedding is used, where$2^{n}$classical data values are encoded into$n$qubits. Compression performance, visual evaluation, and objective evaluation are carried out to assess the proposed compression techniques. Our experimental results show that the crucial patterns in images are retained in the compressed images even at 75% compression. The compressed images can be used for postprocessing tasks using classical or quantum computing algorithms.

IEEE Sensors LettersJan 25, 202318 citations

Satellite Image Representations for Quantum Classifiers

Johann Maximilian Zollner, Paul Walther, Martin Werner

Abstract Existing quantum hardware is limited in the number of bits and length of the series of operations. Nevertheless, by shifting parts of the computation on classical hardware, hybrid quantum-classical systems utilize quantum hardware for scaled-down machine learning approaches, which is quantum machine learning. Due to the theoretically possible computational speed-up of quantum computers compared to classical computers and the increasing volume and speed of data generated in earth observation, attempts are now being made to use quantum computers for satellite image processing. However, satellite imagery is too large and high dimensional, and transformations that reduce the dimensionality are necessary to fit the classical data in the limited input domain of quantum circuits. This paper presents and compares several dimensionality reduction techniques as part of hybrid quantum-classical systems to represent satellite images with up to $256\times 256\times 3$ 256 × 256 × 3 values with only 16 values. We evaluate the representations of two benchmark datasets with supervised classification by four different quantum circuit architectures. We demonstrate the potential use of quantum machine learning for satellite image classification and give a comprehensive overview of the impact of various satellite image representations on the performance of quantum classifiers. It shows that autoencoder models are best suited to create small-scale representations, outperforming commonly used methods such as principle component analysis.Abstract Existing quantum hardware is limited in the number of bits and length of the series of operations. Nevertheless, by shifting parts of the computation on classical hardware, hybrid quantum-classical systems utilize quantum hardware for scaled-down machine learning approaches, which is quantum machine learning. Due to the theoretically possible computational speed-up of quantum computers compared to classical computers and the increasing volume and speed of data generated in earth observation, attempts are now being made to use quantum computers for satellite image processing. However, satellite imagery is too large and high dimensional, and transformations that reduce the dimensionality are necessary to fit the classical data in the limited input domain of quantum circuits. This paper presents and compares several dimensionality reduction techniques as part of hybrid quantum-classical systems to represent satellite images with up to $$256\times 256\times 3$$ 256 × 256 × 3 values with only 16 values. We evaluate the representations of two benchmark datasets with supervised classification by four different quantum circuit architectures. We demonstrate the potential use of quantum machine learning for satellite image classification and give a comprehensive overview of the impact of various satellite image representations on the performance of quantum classifiers. It shows that autoencoder models are best suited to create small-scale representations, outperforming commonly used methods such as principle component analysis.

Datenbank-SpektrumFeb 23, 202417 citations

A Fast Quantum Image Compression Algorithm based on Taylor Expansion

Vu Tuan Hai, Huynh Ho Thi Mong Trinh, Pham Hoai Luan

With the increasing demand for storing images, traditional image compression methods face challenges in balancing the compressed size and image quality. However, the hybrid quantum-classical model can recover this weakness by using the advantage of qubits. In this study, we upgrade a quantum image compression algorithm within parameterized quantum circuits. Our approach encodes image data as unitary operator parameters and applies the quantum compilation algorithm to emulate the encryption process. By utilizing first-order Taylor expansion, we significantly reduce both the computational cost and loss, better than the previous version. Experimental results on benchmark images, including Lenna and Cameraman, show that our method achieves up to 86% reduction in the number of iterations while maintaining a lower compression loss, better for high-resolution images. The results confirm that the proposed algorithm provides an efficient and scalable image compression mechanism, making it a promising candidate for future image processing applications.With the increasing demand for storing images, traditional image compression methods face challenges in balancing the compressed size and image quality. However, the hybrid quantum-classical model can recover this weakness by using the advantage of qubits. In this study, we upgrade a quantum image compression algorithm within parameterized quantum circuits. Our approach encodes image data as unitary operator parameters and applies the quantum compilation algorithm to emulate the encryption process. By utilizing first-order Taylor expansion, we significantly reduce both the computational cost and loss, better than the previous version. Experimental results on benchmark images, including Lenna and Cameraman, show that our method achieves up to 86% reduction in the number of iterations while maintaining a lower compression loss, better for high-resolution images. The results confirm that the proposed algorithm provides an efficient and scalable image compression mechanism, making it a promising candidate for future image processing applications.

arXiv:2502.10684International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)May 20, 20253 citations

Revolutionizing Image Processing with Quantum Algorithms: Encoding, Enhancement and Recognition

Shivani Sood, Ashima Kalra, Gurjeet Singh

Quantum image processing uses quantum computing, which implements the concepts of entanglement and superposition to increase efficiency, scalability, and revolutionary promise in demanding image processing ap plications. In this research paper, a comprehensive frame work for quantum image processing (QIP) is presented, which includes innovative quantum algorithms for pattern recognition, image improvement, and representation. The system presents advanced quantum encoding models for effective image data manipulation and storage. In addition, it discusses the quantum image-based algorithms that give better results than classical techniques in terms of speed and accuracy for image enhancement tasks such as edge recognition, contrast adjustment, and noise reduction. Researchers have developed quantum-based feature ex traction and classification methods for pattern recognition, which demonstrate enhanced scalability and accuracy for complex visual data. It has various applications in satellite data processing, secure visual data transmission, and medical imaging highlighted by simulations and theoretical analysis that confirm the proposed approaches.Quantum image processing uses quantum computing, which implements the concepts of entanglement and superposition to increase efficiency, scalability, and revolutionary promise in demanding image processing ap plications. In this research paper, a comprehensive frame work for quantum image processing (QIP) is presented, which includes innovative quantum algorithms for pattern recognition, image improvement, and representation. The system presents advanced quantum encoding models for effective image data manipulation and storage. In addition, it discusses the quantum image-based algorithms that give better results than classical techniques in terms of speed and accuracy for image enhancement tasks such as edge recognition, contrast adjustment, and noise reduction. Researchers have developed quantum-based feature ex traction and classification methods for pattern recognition, which demonstrate enhanced scalability and accuracy for complex visual data. It has various applications in satellite data processing, secure visual data transmission, and medical imaging highlighted by simulations and theoretical analysis that confirm the proposed approaches.

Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit)Nov 19, 20250 citations

A Quantum-Classical Hybrid Classifier Using Multi-Encoding Method for Images

Níyazí Furkan Bar, Hasan Yetış, Mehmet Karaköse

Image data grows in volume and size more quickly than classical processing power. Thanks to its entanglement and super-position qualities, quantum computing, which is based on quantum physics, has a lot of potential for speed and processing capacity. For high power challenges, it is therefore highly common to try to utilize quantum computing units rather than classical computing units. In this study, a hybrid quantum-classical approach was proposed for utilizing quantum computers’ advantages in image classification. This hybrid technique uses a variational quantum circuit (VQC) on the quantum computer side. To overcome the qubit restriction in the VQC, multiple amplitude encoding was used as the data encoding method. In the classical computer part of the proposed approach, the preprocessing of the image, the convolution operation, and the optimization of the parameters of the single-qubit rotation gates in the VQC were performed. The proposed approach was trained and tested on two different data sets. The accuracy rates acquired in 2-layers and 4-layers VQCs within the data sets were evaluated in the test results. The proposed approach was evaluated against studies that were similar in the literature. Compared to similar studies, it was observed that it is more successful in terms of the number of parameters used and quantum cost. As a result, the effectiveness of the proposed approach was verified.Image data grows in volume and size more quickly than classical processing power. Thanks to its entanglement and super-position qualities, quantum computing, which is based on quantum physics, has a lot of potential for speed and processing capacity. For high power challenges, it is therefore highly common to try to utilize quantum computing units rather than classical computing units. In this study, a hybrid quantum-classical approach was proposed for utilizing quantum computers’ advantages in image classification. This hybrid technique uses a variational quantum circuit (VQC) on the quantum computer side. To overcome the qubit restriction in the VQC, multiple amplitude encoding was used as the data encoding method. In the classical computer part of the proposed approach, the preprocessing of the image, the convolution operation, and the optimization of the parameters of the single-qubit rotation gates in the VQC were performed. The proposed approach was trained and tested on two different data sets. The accuracy rates acquired in 2-layers and 4-layers VQCs within the data sets were evaluated in the test results. The proposed approach was evaluated against studies that were similar in the literature. Compared to similar studies, it was observed that it is more successful in terms of the number of parameters used and quantum cost. As a result, the effectiveness of the proposed approach was verified.

Feb 15, 202310 citations

2^{n}

classical data values are encoded into

n

qubits. Compression performance, visual evaluation, and objective evaluation are carried out to assess the proposed compression techniques. Our experimental results show that the crucial patterns in images are retained in the compressed images even at 75% compression. The compressed images can be used for postprocessing tasks using classical or quantum computing algorithms.

256\times 256\times 3

256 × 256 × 3 values with only 16 values. We evaluate the representations of two benchmark datasets with supervised classification by four different quantum circuit architectures. We demonstrate the potential use of quantum machine learning for satellite image classification and give a comprehensive overview of the impact of various satellite image representations on the performance of quantum classifiers. It shows that autoencoder models are best suited to create small-scale representations, outperforming commonly used methods such as principle component analysis.