A video encoding and decoding method and device based on image super-resolution
A super-resolution, video coding technology, used in image communication, digital video signal modification, electrical components, etc., can solve the blurring of edges, restrict the efficiency of sub-pixel motion compensation, and it is difficult to restore high-frequency detail information of high-resolution images, etc. problem, to avoid blurred edges, improve accuracy, and improve coding efficiency
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Embodiment 1
[0023] Please refer to figure 1 , figure 1 It is a flowchart of an image super-resolution-based video encoding method in an embodiment. Such as figure 1 As shown, this embodiment provides a video coding method based on image super-resolution, which may include the following steps:
[0024] 101. Using a pre-trained texture dictionary library to perform super-resolution interpolation processing on video images.
[0025] After super-resolution interpolation processing, the reference image is obtained. The texture dictionary library includes: one or more sets of dictionary bases, the dictionary bases are: a mapping group consisting of high-resolution image blocks of training images and low-resolution image blocks corresponding to the high-resolution image blocks, The super-resolution interpolation processing includes: image enlargement and image detail information restoration.
[0026] 102. Perform motion estimation and motion compensation on the reference image for each imag...
Embodiment approach
[0034] S1. Select a plurality of high-resolution local image blocks from a training image set including several training images, wherein a high-resolution local image block is composed of at least two pixels on the image where it is located. The training image is down-sampled to obtain low-resolution local image blocks corresponding to each local image block.
[0035] S2. Extracting local features of high-resolution local image blocks to obtain high-resolution dictionary samples Dh(y), and extracting local features of low-resolution local image blocks corresponding to each of the local image blocks to obtain Low-resolution dictionary samples Dl(y), mapping and combining the high-resolution dictionary samples and the low-resolution dictionary samples to obtain a set of dictionary base samples, the local features include LBS and SES.
[0036] S3. Perform training on the multiple sets of dictionary base samples to obtain a texture dictionary library.
[0037] The process and princ...
Embodiment 2
[0062] Please refer to image 3 , image 3 It is a flowchart of an implementation manner of step 101 in the first embodiment. In this embodiment, each dictionary base in the texture dictionary is classified according to the local features of the high-resolution image blocks of each training image and the local features of the low-resolution image blocks corresponding to the high-resolution image blocks, the Local features include local binary structure and sharp edge structure.
[0063] The video encoding method based on image super-resolution provided in this embodiment uses a pre-trained texture dictionary library to perform super-resolution interpolation processing on video images, which may specifically include the following steps:
[0064] 101a. Extract local features of each image block on the video image.
[0065] 101b. Match the local features of each image block in the video image with the local features of each dictionary base in the texture dictionary library, an...
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