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Related Concept Videos

Downsampling01:20

Downsampling

222
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
222
Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Encoding01:19

Encoding

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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

341
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
341
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

294
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...
294
Block Diagram Reduction01:22

Block Diagram Reduction

265
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
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Digital Image Decoder for Efficient Hardware Implementation.

Goran Savić1, Milan Prokin1, Vladimir Rajović1

  • 1School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

New hardware architectures for digital image decoders reduce logic and memory resource usage. This efficient digital image decoder implementation uses less memory and logic, outperforming state-of-the-art solutions.

Keywords:
digital image decoderefficient hardware implementationimage decompression

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Area of Science:

  • Digital image processing
  • Hardware architecture design
  • Video compression

Background:

  • Increasing digital image resolution and video frame rates demand more logical and memory resources for decompression.
  • Existing hardware architectures for digital image decoding face challenges in resource efficiency.

Purpose of the Study:

  • To present a novel digital image decoder architecture optimized for efficient hardware implementation.
  • To reduce the logical and memory resources required for digital image and video decompression.

Main Methods:

  • Development of specialized hardware blocks including entropy decoder, decoding probability estimator, dequantizer, and inverse subband transformer.
  • Hardware implementation of the proposed digital image decoder on a low-cost FPGA device.

Main Results:

  • The proposed inverse subband transformer requires 20% less memory capacity and fewer logic resources than state-of-the-art implementations.
  • The implemented digital image decoder uses at least 32% less memory than other high-definition capable decoders.
  • The decoder achieves memory usage lower than even smaller-frame-size decoders and maintains competitive operating frequencies.

Conclusions:

  • The developed digital image decoder architecture offers significant reductions in memory and logic resource utilization.
  • This efficient hardware implementation is suitable for resource-constrained environments and high-definition video processing.
  • The proposed design represents a notable advancement in efficient digital image decompression hardware.