Load balancing method for video decoding in a system providing hardware and software decoding resources

    公开(公告)号:US12238312B2

    公开(公告)日:2025-02-25

    申请号:US18355005

    申请日:2023-07-19

    Abstract: A load balancing method for video decoding. The load balancing includes first determining which hardware devices are suitable for the new decoding process, and determining the current load of each of the suitable hardware devices. From the suitable devices potential devices are selected having a current load less than a threshold and overloaded devices are selected having a load greater than or equal to the threshold. If there are no suitable devices, then the decoding process is implemented by software decoding. If the list of potential hardware devices includes only one potential hardware device, then the decoding process is implemented on the hardware device. If the list of potential hardware devices includes more than one potential hardware device, then it is determined how many decoding processes are currently running on each potential hardware device, and the new decoding process is implemented on the potential hardware device having the fewest processes.

    METHOD AND SYSTEM FOR LEARNED VIDEO COMPRESSION

    公开(公告)号:US20240430463A1

    公开(公告)日:2024-12-26

    申请号:US18338731

    申请日:2023-06-21

    Abstract: There is provided a computer-implemented method for learned video compression, which includes processing a current frame (xt) and previously decoded frame ({circumflex over (x)}t−1) of a video data using a motion estimation model to estimate a motion vector (vt) for every pixel, compressing the motion vector (vt) and reconstructing the motion vector (vt) to a reconstructed motion vector ({circumflex over (v)}t), applying an enhanced context mining (ECM) model to obtain enhanced context ({umlaut over (C)}E) from the reconstructed motion vector ({circumflex over (v)}t) and previously decoded frame feature (x̆t−1), compressing the current frame (xt) with the assistance of the enhanced context ({umlaut over (C)}E) to obtain a reconstructed frame ({circumflex over (x)}t′), and providing the reconstructed frame ({circumflex over (x)}t′) to a post-enhancement backend network to obtain a high-resolution frame ({circumflex over (x)}t).

    Device and method for encoding and decoding image using AI

    公开(公告)号:US12170786B2

    公开(公告)日:2024-12-17

    申请号:US18133369

    申请日:2023-04-11

    Abstract: An image decoding method includes obtaining feature data of a current optical flow and feature data of a current residual image from a bitstream; obtaining the current optical flow and first weight data by applying the feature data of the current optical flow to an optical flow decoder; obtaining the current residual image by applying the feature data of the current residual image to a residual decoder; obtaining a preliminary prediction image from the previous reconstructed image, based on the current optical flow; obtaining a final prediction image by applying sample values of the first weight data to sample values of the preliminary prediction image; and obtaining a current reconstructed image corresponding to the current image by combining the final prediction image with the current residual image.

    Method and system for image compressing and coding with deep learning

    公开(公告)号:US12155849B2

    公开(公告)日:2024-11-26

    申请号:US18012316

    申请日:2020-06-25

    Inventor: Yun Li

    Abstract: An image processing system (100) and methods therein for compressing and coding an image and for optimizing its parameters are disclosed. The embodiments herein provide an improved system and simplified method with deterministic uniform quantization with integer levels based on Softmax function for image compression and coding. The embodiments herein produce exact discrete probability mass function for latent variables to be coded from side information. The embodiments herein enable training of the image processing system to minimize the bit rate through backpropagation at the same time. Moreover, the embodiments herein create the possibility to encode region of interest (ROI) areas during coding.

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