Intra/inter mode decision for predictive frame encoding

    公开(公告)号:US12184840B2

    公开(公告)日:2024-12-31

    申请号:US17875305

    申请日:2022-07-27

    Abstract: This invention predicts that intra mode prediction is more effective for the macroblocks where motion estimation in inter mode prediction fails. This failure is indicated by a large value of the inter mode SAD. This invention performs intra mode prediction for only macro blocks have larger inter mode SADs. The definition of a large inter mode SAD differs for different content. This invention compares the inter mode SAD of a current macroblock with an adaptive threshold. This adaptive threshold depends on the average and variance of the SADs of the previous predicted frame. An adaptive threshold is calculated for each new predictive frame.

    Method and system of bit rate control

    公开(公告)号:US12047566B2

    公开(公告)日:2024-07-23

    申请号:US17524618

    申请日:2021-11-11

    CPC classification number: H04N19/115 H04N19/149 H04N19/172

    Abstract: A method and system for bit rate control during encoding of multimedia data are disclosed. A change in complexity of a multimedia picture relative to complexity associated with one or more multimedia pictures in a multimedia sequence is determined. A complexity associated with a multimedia picture is determined based on number of bits and an average quantization associated with the multimedia picture. A bit rate is adjusted for encoding the multimedia picture based on the change in complexity of the multimedia picture. The bit rate is increased on determining an increase in complexity of the multimedia picture and is decreased on determining a decrease in complexity of the multimedia picture. Utilization of additional bits during the increase in the bit rate and saving of bits during the decrease in the bit rate are compensated during adjusting of bit rates for encoding subsequent multimedia pictures in the multimedia sequence.

    EFFICIENT OBJECT DETECTION USING DEEP LEARNING TECHNIQUES

    公开(公告)号:US20220147748A1

    公开(公告)日:2022-05-12

    申请号:US17512049

    申请日:2021-10-27

    Abstract: Various embodiments of the present technology relate to using neural networks to detect objects in images. More specifically, some embodiments relate to the reduction of computational analysis regarding object detection via neural networks. In an embodiment, a method of performing object detection is provided. The method comprises determining, via a convolution neural network, at least a classification of an image, wherein the classification corresponds to an object in the image and comprises location vectors corresponding to pixels of the image. The method also comprises, for at least a location vector of the location vectors, obtaining a confidence level, wherein the confidence level represents a probability of the object being present at the location vector, and calculating an upper-bound score based at least on the confidence level. The method further comprises, for at least an upper-bound score based at least on the confidence level, performing an activation function on the upper-bound score, and classifying, via a detection layer, the object in the image.

    Rate Control in Video Coding
    9.
    发明申请

    公开(公告)号:US20210037252A1

    公开(公告)日:2021-02-04

    申请号:US17075053

    申请日:2020-10-20

    Abstract: A method of rate control in coding of a video sequence to generate a compressed bit stream is provided that includes computing a sequence base quantization step size for a sequence of pictures in the video sequence, computing a picture base quantization step size for a picture in the sequence of pictures based on the sequence base quantization step size, a type of the picture, and a level of the picture in a rate control hierarchy, and coding the picture using the picture base quantization step size to generate a portion of the compressed bit stream.

    Stationary-vehicle structure from motion

    公开(公告)号:US10657389B2

    公开(公告)日:2020-05-19

    申请号:US16434542

    申请日:2019-06-07

    Abstract: A vehicular structure from motion (SfM) system can store a number of image frames acquired from a vehicle-mounted camera in a frame stack according to a frame stack update logic. The SfM system can detect feature points, generate flow tracks, and compute depth values based on the image frames, the depth values to aid control of the vehicle. The frame stack update logic can select a frame to discard from the stack when a new frame is added to the stack, and can be changed from a first in, first out (FIFO) logic to last in, first out (LIFO) logic upon a determination that the vehicle is stationary. An optical flow tracks logic can also be modified based on the determination. The determination can be made based on a dual threshold comparison to insure robust SfM system performance.

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