Related papers: Quantum walk inspired JPEG compression of images

Quantum walk inspired JPEG compression of images

URL: http://arxiv.org/abs/2602.12306v1
Date: Thu, 12 Feb 2026 05:40:33 GMT
Title: Quantum walk inspired JPEG compression of images
Authors: Abhishek Verma, Sahil Tomar, Sandeep Kumar,
Abstract summary: The proposed framework is evaluated on MNIST, CIFAR10, and ImageNet subsets, using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bits Per Pixel (BPP), and error heatmap visual analysis as evaluation metrics.<n> Experimental results show average gains ranging from 3 to 6 dB PSNR, along with better structural preservation of edges, contours, and luminance transitions, without modifying decoder compatibility.
Score: 2.928053186719895
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: This work proposes a quantum inspired adaptive quantization framework that enhances the classical JPEG compression by introducing a learned, optimized Qtable derived using a Quantum Walk Inspired Optimization (QWIO) search strategy. The optimizer searches a continuous parameter space of frequency band scaling factors under a unified rate distortion objective that jointly considers reconstruction fidelity and compression efficiency. The proposed framework is evaluated on MNIST, CIFAR10, and ImageNet subsets, using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bits Per Pixel (BPP), and error heatmap visual analysis as evaluation metrics. Experimental results show average gains ranging from 3 to 6 dB PSNR, along with better structural preservation of edges, contours, and luminance transitions, without modifying decoder compatibility. The structure remains JPEG compliant and can be implemented using accessible scientific packages making it ideal for deployment and practical research use.

Related papers

Hybrid Quantum Image Preparation via JPEG Compression [0.0]
We present a hybrid classical-quantum image preparation scheme that reduces the quantum implementation cost of image loading for quantum pixel information encoding (QPIE)<n>The proposed method, termed JPEG-assisted QPIE (JQPIE), loads only the quantized JPEG coefficients into a quantum register.
arXiv Detail & Related papers (2026-02-05T21:23:58Z)
Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation [75.58269386927076]
Autoregressive (AR) models are often dismissed as impractical due to prohibitive computational cost.<n>This work re-thinks this paradigm, introducing a framework built on hierarchical parallelism and progressive adaptation.<n> Experiments on diverse datasets (natural, satellite, medical) validate that our method achieves new state-of-the-art compression.
arXiv Detail & Related papers (2025-11-14T06:27:58Z)
Pathology Image Compression with Pre-trained Autoencoders [52.208181380986524]
Whole Slide Images in digital histopathology pose significant storage, transmission, and computational efficiency challenges.<n>Standard compression methods, such as JPEG, reduce file sizes but fail to preserve fine-grained phenotypic details critical for downstream tasks.<n>In this work, we repurpose autoencoders (AEs) designed for Latent Diffusion Models as an efficient learned compression framework for pathology images.
arXiv Detail & Related papers (2025-03-14T17:01:17Z)
Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels. They are not widely adopted by general users due to their substantial storage requirements. We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
arXiv Detail & Related papers (2023-06-21T06:59:07Z)
Learned Lossless Compression for JPEG via Frequency-Domain Prediction [50.20577108662153]
We propose a novel framework for learned lossless compression of JPEG images. To enable learning in the frequency domain, DCT coefficients are partitioned into groups to utilize implicit local redundancy. An autoencoder-like architecture is designed based on the weight-shared blocks to realize entropy modeling of grouped DCT coefficients.
arXiv Detail & Related papers (2023-03-05T13:15:28Z)
JND-Based Perceptual Optimization For Learned Image Compression [42.822121565430926]
We propose a JND-based perceptual quality loss for learned image compression schemes. We show that the proposed method has led to better perceptual quality than the baseline model under the same bit rate.
arXiv Detail & Related papers (2023-02-25T14:49:09Z)
High-Perceptual Quality JPEG Decoding via Posterior Sampling [13.238373528922194]
We propose a different paradigm for JPEG artifact correction. We aim to obtain sharp, detailed and visually reconstructed images, while being consistent with the compressed input. Our solution offers a diverse set of plausible and fast reconstructions for a given input with perfect consistency.
arXiv Detail & Related papers (2022-11-21T19:47:59Z)
Estimating the Resize Parameter in End-to-end Learned Image Compression [50.20567320015102]
We describe a search-free resizing framework that can further improve the rate-distortion tradeoff of recent learned image compression models. Our results show that our new resizing parameter estimation framework can provide Bjontegaard-Delta rate (BD-rate) improvement of about 10% against leading perceptual quality engines.
arXiv Detail & Related papers (2022-04-26T01:35:02Z)
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder [73.48927855855219]
We propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends. Experiments demonstrate that our approach successfully improves the rate-distortion performance over JPEG across various quality metrics.
arXiv Detail & Related papers (2022-01-27T20:20:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.