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Multi-weight inter-component self-prediction data compression method and device capable of reducing number of components
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A self-prediction and component technology, applied in the field of encoding and decoding systems, can solve problems such as limited functions
Pending Publication Date: 2020-09-18
上海天荷电子信息有限公司
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Therefore, the usefulness of existing inter-component predictions is limited
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Embodiment 1
[0078] In the encoding method or device or the decoding method or device, the data with L components is an image or video with three components, namely L=3, with one main component and two sub-components; the K is 3 or 2; when K is 2, the K components are one of the principal component and the two secondary components or the two secondary components.
Embodiment 2
[0080] In the encoding method or device or the decoding method or device, the data with L components is an image or video with a total of three components, i.e. L=3, with one main component and two secondary components, and the three components are One of the following situations:
[0081] Y luma component, U chroma component, V chroma component,
[0082] Y luma component, Cb chroma component, Cr chroma component,
[0083] Y luma component, Cb chroma component, Cr chroma component,
[0084] R red component, G green component, B blue component,
[0085] G green component, R red component, B blue component,
[0086] G green component, B blue component, R red component,
[0087] The K is 3 or 2; when K is 2, the K components are one of the principal component and the two secondary components or the two secondary components.
Embodiment 3
[0089] In the encoding method or device or decoding method or device,
[0090] The data with L components is an image or video with a total of three components, namely L=3, with one main component and two sub-components, the K is 2, and the K components are the two sub-components;
[0091] The self-predictive encoding between two subcomponents is to construct the prediction value of another subcomponent from one subcomponent, at least according to the selected specific weight value, resulting in 1 prediction difference or 2 prediction differences, while the two The self-predictive decoding between subcomponents is at least according to the selected specific weight value, at least from the reconstructed value of 1 predicted difference value or from the reconstructed value of 2 predicted difference values, through an operation including weighted combination, to generate two The reconstructed value of the subcomponent.
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Abstract
The invention provides a data compression method and device for dynamically selecting one of a plurality of weight values at a coding and decoding block level to perform inter-component self-prediction of multi-component data so as to reduce the number of components. In the self-prediction of the plurality of components, each component generates a prediction difference value by using the weightedvalues of the other internal components. When a plurality of predetermined weight values in the plurality of weight values are selected, at least one prediction difference component with a very smallnumerical value close to zero is abandoned, so that the number of components is reduced, the data volume is greatly reduced, and the coding efficiency is improved.
Description
technical field [0001] The invention relates to a coding and decoding system for lossy or lossless compression of data, especially a method and device for coding and decoding multi-component data such as color image and video data. Background technique [0002] As human society enters the era of big data, cloud computing, mobile computing, cloud-mobile computing, ultra-high-definition (4K) and ultra-high-definition (8K) video image resolution, 4G / 5G communication, and virtual reality, all kinds of data , including big data, image data, and video data, ultra-high compression ratio and extremely high-quality data compression has become an indispensable technology. [0003] A data set is a collection of data samples, also known as data elements (for example: bytes, bits, pixels, spatial sampling points, transform domain coefficients). For a data set arranged in a certain space (one-dimensional, two-dimensional, or multi-dimensional) shape and with a certain number of elements ...
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