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Related Concept Videos

Downsampling01:20

Downsampling

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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.
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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...
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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...
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Encoding01:19

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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.
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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.
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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An Efficient Compressive Sensed Video Codec with Inter-Frame Decoding and Low-Complexity Intra-Frame Encoding.

Evgeny Belyaev1

  • 1Information Technologies and Programming Faculty, ITMO University, Kronverksky Pr. 49, bldg. A, St. Petersburg 197101, Russia.

Sensors (Basel, Switzerland)
|February 11, 2023
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Summary

This study introduces CS-JPEG, a novel video codec using compressive sensing (CS). It achieves faster encoding speeds than traditional codecs while improving rate-distortion performance, making it ideal for resource-constrained devices.

Keywords:
compressive sensingfast video encoding

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Area of Science:

  • Digital signal processing
  • Video compression
  • Information theory

Background:

  • Traditional video codecs like H.264/AVC and H.265/HEVC face limitations in encoding speed and computational demands.
  • Compressive sensing (CS) offers a theoretical framework for reconstructing signals from fewer samples than required by the Nyquist-Shannon theorem.
  • Existing CS-based video codecs struggle to match the rate-distortion performance of conventional methods.

Purpose of the Study:

  • To develop a novel video codec, CS-JPEG, that leverages compressive sensing (CS) for efficient video encoding.
  • To overcome the rate-distortion performance limitations of previous CS-based video codecs.
  • To provide a viable alternative for video applications with strict computational or power constraints.

Main Methods:

  • Development of a new video codec, CS-JPEG, based on the compressive sensing (CS) framework.
  • Implementation of measurement acquisition, compression, and video reconstruction strategies within the CS paradigm.
  • Comparative performance evaluation against established video codecs: Motion JPEG (MJPEG), H.264/AVC, and H.265/HEVC.

Main Results:

  • CS-JPEG demonstrates significantly faster encoding times: 2.2x (MJPEG), 1.9x (H.264/AVC), and 30.5x (H.265/HEVC).
  • The codec achieves improved rate-distortion performance, with peak signal-to-noise ratio (PSNR) gains of 2.33 dB (MJPEG), 0.79 dB (H.264/AVC), and 1.45 dB (H.265/HEVC).
  • CS-JPEG represents the first CS-based codec to combine fast encoding with high rate-distortion efficiency.

Conclusions:

  • CS-JPEG offers a compelling solution for video coding, balancing speed and quality.
  • The proposed codec is particularly suitable for applications with limited computational resources or battery life, such as upstreaming devices.
  • This work advances the practical application of compressive sensing in video compression technology.