Jove
Visualize
Contact Us
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

Related Concept Videos

Linearization and Approximation01:26

Linearization and Approximation

Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

You might also read

Related Articles

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

Sort by
Same author

Supply and demand for liver transplant surgery: are we training enough surgeons?

HPB : the official journal of the International Hepato Pancreato Biliary Association·2008
Same author

The fate of patients who undergo "preoperative" ERCP to clear known or suspected bile duct stones.

Surgical endoscopy·2008
Same author

Reduced storage VQ via secondary quantization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

An analysis of some common scanning techniques for lossless image coding.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Endogenous acyl ghrelin is involved in mediating spontaneous phase III-like contractions of the rat stomach.

Neurogastroenterology and motility·2007
Same author

Laparoscopic distal pancreatectomy with splenic preservation.

Surgical endoscopy·2007
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Videos

Least-squares model-based halftoning.

T N Pappas1, D L Neuhoff

  • 1Bell Labs., Lucent Technol., Murray Hill, NJ 07974, USA. pappas@bell-labs.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
Summary
This summary is machine-generated.

A novel least-squares model-based (LSMB) digital halftoning method improves image quality by minimizing errors. This approach enhances textures and resolution compared to traditional techniques.

Related Experiment Videos

Area of Science:

  • Digital image processing
  • Computer vision
  • Perceptual modeling

Background:

  • Conventional digital halftoning techniques often struggle with texture artifacts and limited resolution.
  • Error diffusion methods, while common, present their own set of image reproduction challenges.

Purpose of the Study:

  • To introduce a least-squares model-based (LSMB) approach for digital halftoning.
  • To optimize halftoned image reproduction by minimizing the squared error between printer/visual models and the original image.
  • To address limitations of existing methods in achieving high-fidelity gray-scale and spatial resolution.

Main Methods:

  • Developed a least-squares model-based (LSMB) digital halftoning algorithm.
  • Utilized printer and visual perception models to guide the halftoning process.
  • Employed iterative techniques to solve the two-dimensional (2-D) least-squares problem, as the Viterbi algorithm is not applicable.
  • Investigated algorithm performance across various viewing distances and printer resolutions.

Main Results:

  • LSMB halftoning demonstrated superior texture quality and enhanced spatial and gray-scale resolution compared to conventional methods.
  • The least-squares approach effectively mitigated common issues associated with error diffusion techniques.
  • Precise control over image sharpness was achieved using the LSMB method.
  • Performance was evaluated across a spectrum of viewing distances and printer resolutions.

Conclusions:

  • The proposed LSMB approach offers a significant advancement in digital halftoning quality.
  • LSMB provides a robust method for improving image fidelity, texture, and resolution.
  • This technique offers superior control over image sharpness and reduces artifacts, making it a valuable alternative to existing halftoning algorithms.