Showing 4 open source projects for ".bcm compression source code"

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    VidGear

    VidGear

    A High-performance cross-platform Video Processing Python framework

    VidGear is a high-performance Python framework that provides a unified and extensible solution for building real-time video processing applications. It acts as an abstraction layer over powerful multimedia libraries such as OpenCV, FFmpeg, and ZeroMQ, simplifying complex workflows into concise and efficient APIs. The framework is built around modular components called “gears,” each responsible for tasks such as video capture, streaming, encoding, and network transmission. It supports...
    Downloads: 0 This Week
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  • 2
    PI-Based Image Encoder / Converter

    PI-Based Image Encoder / Converter

    Python code able to convert / compress image to PI (3.14, π) Indexes

    Image processing tool that encodes pixel data as indices within the first 16.7 million digits of PI (π). Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro...
    Downloads: 5 This Week
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  • 3
    LAME (Lame Aint an MP3 Encoder)

    LAME (Lame Aint an MP3 Encoder)

    A high quality MP3 encoder

    LAME is an educational tool to be used for learning about MP3 encoding. The goal of the LAME project is to improve the psycho acoustics, quality and speed of MP3 encoding. Note: we provide source code only!
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    Downloads: 29,742 This Week
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  • 4
    EnCodec

    EnCodec

    State-of-the-art deep learning based audio codec

    Encodec is a neural audio codec developed by Meta for high-fidelity, low-bitrate audio compression using end-to-end deep learning. Unlike traditional codecs (like MP3 or Opus), Encodec uses a learned quantizer and decoder to reconstruct complex waveforms with remarkable accuracy at bitrates as low as 1.5 kbps. It employs a convolutional encoder–decoder architecture trained with perceptual loss functions that optimize for human auditory quality rather than raw waveform distance. The model can...
    Downloads: 3 This Week
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