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Related Concept Videos

Complex Zeros01:29

Complex Zeros

Complex zeros are the solutions to polynomial equations that include imaginary numbers, specifically, numbers of the form a + bi, where a and b are real numbers and i is the imaginary unit defined by i2=-1. These zeros satisfy the equation P(x) = 0, where P(x) is a polynomial with real or complex coefficients. Since the complex number system includes all real numbers, it provides a complete framework for analyzing all possible roots of a polynomial.Every polynomial of degree n≥1 can be...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Even and Odd Signals01:17

Even and Odd Signals

An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the denominator.
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Related Experiment Videos

Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs.

Aaron T Deever1, Sheila S Hemami

  • 1Eastman Kodak Co., Rochester, NY 14650-1816, USA. aaron.deever@kodak.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
Summary
This summary is machine-generated.

This study enhances wavelet image coding by improving the efficiency of coding transform coefficient signs. New methods yield significant performance gains for embedded wavelet codecs.

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Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Computer Vision

Background:

  • Wavelet transform coefficients have magnitude and sign components.
  • Current wavelet image coding struggles with efficient sign coding.
  • Entropy coding of signs is often overlooked for compression gains.

Purpose of the Study:

  • To investigate and improve the sign coding of wavelet transform coefficients.
  • To enhance the efficiency of embedded wavelet image coders.
  • To explore new context modeling techniques for sign coding.

Main Methods:

  • Examined sign coding in embedded wavelet image coders.
  • Developed a context model using intraband wavelet coefficients.
  • Introduced a projection technique for nonintraband coefficient inclusion.
  • Utilized accumulated sign prediction statistics at the decoder.

Main Results:

  • Achieved average Peak Signal-to-Noise Ratio (PSNR) improvements of 0.3 dB.
  • Demonstrated effectiveness of intraband and nonintraband coefficient context models.
  • Showcased decoder-side statistics for improved reconstruction estimates.
  • Validated applicability across various embedded wavelet image codecs.

Conclusions:

  • Improved sign coding significantly enhances wavelet image compression.
  • The proposed projection technique and context modeling are effective.
  • These methods offer a valuable approach for optimizing embedded wavelet codecs.