Jove
Visualize
Contact Us

Related Experiment Video

Updated: May 28, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Image prediction based on neighbor-embedding methods.

Mehmet Türkan1, Christine Guillemot

  • 1National Institute for Research in Computer Science and Control, INRIA/IRISA, Rennes, France. Mehmet.Turkan@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 11, 2011
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multispectral extended depth-of-field fluorescence microscopy with co-designed meta-optics and neural reconstruction.

Light, science & applications·2026
Same author

Automated smartphone based cell analysis platform.

Npj imaging·2025
Same author

Learning flat optics for extended depth of field microscopy imaging.

Nanophotonics (Berlin, Germany)·2024
Same author

Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

HEADSET: Human Emotion Awareness under Partial Occlusions Multimodal DataSET.

IEEE transactions on visualization and computer graphics·2023
Same author

Advanced Scalability for Light Field Image Coding.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Two novel intra-image prediction methods using nonnegative matrix factorization (NMF) and locally linear embedding improve image coding. These methods enhance the peak signal-to-noise ratio (PSNR) by up to 3 dB compared to existing techniques.

Area of Science:

  • Image processing
  • Data compression
  • Machine learning

Background:

  • Intra-image prediction is crucial for efficient image coding.
  • Existing methods like H.264/AVC intraprediction and template matching have limitations.
  • Dimensionality reduction techniques offer potential for improved prediction accuracy.

Purpose of the Study:

  • To introduce and evaluate two new intra-image prediction methods.
  • To leverage nonnegative matrix factorization (NMF) and locally linear embedding for prediction.
  • To analyze the impact of parameters like 'k' and constraints on prediction performance.

Main Methods:

  • Developed two intra-image prediction algorithms based on NMF and locally linear embedding.
  • Approximated image blocks using linear combinations of k-nearest neighbors.

Related Experiment Videos

Last Updated: May 28, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

  • Analyzed the influence of the 'k' parameter and nonnegativity/sum-to-one constraints.
  • Integrated and evaluated methods within a complete image coding-decoding framework.

    • Main Results:

    • Achieved prediction gains up to 2 dB over H.264/AVC intraprediction.
    • Demonstrated gains up to 3 dB compared to template matching.
    • Showed gains up to 1 dB relative to a sparse prediction method.
    • Evaluated Rate-Distortion (RD) performance of the new methods.

    Conclusions:

    • The proposed NMF and locally linear embedding-based prediction methods offer significant performance improvements.
    • These methods provide a valuable alternative for enhancing image coding efficiency.
    • The study highlights the effectiveness of dimensionality reduction in intra-image prediction.