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Neural networks boost JPEG 2000 compression efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method to enhance lossy image compression within the JPEG 2000 standard by integrating neural network-assisted lifting steps. These additional steps are designed to reduce residual redundancy and improve image quality at lower resolutions. The approach, which uses compact neural networks with a single set of trained parameters applicable across all decomposition levels and bit-rates, has demonstrated an average bit-rate saving of up to 17.4% while preserving the scalability features of JPEG 2000. AI

IMPACT This research could lead to more efficient image compression techniques, potentially impacting storage and transmission of visual data.

RANK_REASON Academic paper detailing a novel method for image compression. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Neural networks boost JPEG 2000 compression efficiency

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Academic paper detailing a novel method for image compression. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xinyue Li, Aous Naman, David Taubman ·

    Neural Network Assisted Lifting Steps For Improved Fully Scalable Lossy Image Compression in JPEG 2000

    arXiv:2403.01647v2 Announce Type: replace Abstract: This work proposes to augment the lifting steps of the conventional wavelet transform with additional neural network assisted lifting steps. These additional steps reduce residual redundancy (notably aliasing information) amongs…

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