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量化过程
a quantization unit configured to generate edge image data by performing a quantization process on the edge intensity image data,
量化单元,用于通过对边缘强度图像数据进行量化过程而生成边缘图像数据,
Because this quantization process is applied individually in each block, neighboring blocks quantize coefficients differently.
因為這個量化的處理過程是被單獨的應用在每一個區塊,所以相鄰區塊係數的量化皆不同。
Further, the image processing unit may have an edge extraction unit configured to generate edge intensity image data by performing an edge extraction process on the captured image data, a quantization unit configured to generate edge image data by performing a quantization process on the edge intensity image data, an edge labeling unit configured to regard edge pixel regions having connectivity with the edge image data as a same region, to perform a process of giving a same label number to edge pixels of the same region, and to generate labeled image data, a first rectangle objectification unit configured to perform a process of giving a circumscribed rectangle to the edge pixel regions to which the same label number is given based on the labeled image data, and to generate a rectangular object, and a first fine object removal unit configured to perform a process on the edge image data to remove an edge pixel region having a small number of connected pixels and an edge pixel region in a rectangular object having short vertical and horizontal widths as noise based on the labeled image data and the rectangular object, and to generate output edge image data.
This part of the process is often called "inverse quantization" or "dequantization", although quantization is an inherently non-invertible process.
这个过程被称为反量化,尽管量化本身已经注定是个不可逆过程。
The edge labeling unit 203 regards edge pixel regions having connectivity on the edge image data generated by the quantization unit 202 as the same region, and performs a process of giving the same label number to edge pixels in the same region and thereby generates labeled image data.
The fine object removal unit 205 performs a noise removal process on the edge image data generated by the quantization unit 202 and the labeled image data generated by the edge labeling unit 203, and thereby generates output edge image data and output labeled image data.
The entire process of transform and quantization can be carried out using 16-bit integer and only a single multiply per coefficient without any loss of accuracy and inverse transform mismatch.
FIG. 2 is a functional block diagram of an encoding process 200 including static content detection in accordance with various embodiments of the present disclosure. In this diagram, static content detection is primarily performed by skip decision functionality 202 operating in conjunction with hash code storage 204. Briefly, in various embodiments a skip decision may be reached by comparing hash codes/check values (or the like) of (1) a current input macro-block or slice and (2) a co-located previous input macro-block or slice in order to identify a static portion(s) of video input. In one alternate embodiment, a skip decision may involve comparing hash codes of (1) a reconstructed version of an input macro-block or slice and (2) a reconstructed version of a co-located previous macro-block or slice. In such an embodiment, the reconstructed version of the input macro-block or slice may be generated by prediction, transform and quantization functionality 210. Details of an embodiment of skip decision functionality 202 are described below in conjunction with FIG. 3.
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