Gradable video coding system based on multi-scale online dictionary learning

A technology of dictionary learning and video coding, which is applied in the direction of digital output to display devices, etc., can solve the problems that video coding and compression cannot be applied, and achieve the effects of accelerating convergence and stability, improving representation accuracy, and improving performance and practicability

Active Publication Date: 2014-12-10
SHANGHAI JIAO TONG UNIV
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However, the multi-scale learning dictionary is only suitable for sparse re

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  • Gradable video coding system based on multi-scale online dictionary learning
  • Gradable video coding system based on multi-scale online dictionary learning
  • Gradable video coding system based on multi-scale online dictionary learning

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Embodiment Construction

[0021] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0022] Such as figure 1 , figure 2 As shown, this embodiment provides a scalable video coding system based on multi-scale online dictionary learning, including: a multi-scale training set construction module based on hierarchical sparseness, an online dictionary learning module, and a cross-scale video frame reconstruction module, wherein :

[0023] The multi-scale training set construction module based on hierarchical sparseness uses multi-level wavelet transform to obtain hierarchical sparse ...

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Abstract

The invention provides a gradable video coding system based on multi-scale online dictionary learning. A multi-scale training set establishing module based on layered sparsity is used for obtaining layered sparsity structures, in different scales, of an image through wavelet transformation. By means of a Gaussian differential filter set, direction energy is extracted so that primitive areas in the image can be obtained, and a multi-scale training set is generated by cutting out image blocks of the primitive areas. By means of an online dictionary learning module, it is ensured that dictionary atoms are iterated and optimized under low complexity according to the stochastic gradient descent method so that a sub-dictionary base corresponding to the multi-scale training set can be generated. For low-frequency video frames, a cross-scale video frame reconstruction module learns lost high-frequency information on different levels through the constructed sub-dictionary base; the aim of grading video quality is achieved through different-grade wavelet inverse transformation reconstruction. By means of the gradable video coding system, complexity of a super-resolution algorithm based on learning is lowered, the reconstruction quality gain is obtained at different transmission rates compared with H.264, and high expandability is achieved.

Description

technical field [0001] The present invention relates to a scalable video coding scheme, in particular to a scalable video coding system based on multi-scale online dictionary learning. Background technique [0002] With the improvement of the HEVC standard, the formulation of the HEVC scalable coding scheme has also received extensive attention. In order to adaptively meet the requirements of video transmission on heterogeneous networks with different transmission characteristics and the application requirements of different clients, the scalability of video coding has high theoretical research and practical application value. From H.264 / AVC to HEVC, more and more mature inter-frame and intra-frame prediction methods have improved rate-distortion performance, such as adaptive kernel functions: MDDT, ROT and adaptive DCT / DST transformation, etc., which are essentially How to effectively and sparsely express natural signals by analyzing dictionary bases or learning linear com...

Claims

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Application Information

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IPC IPC(8): G06F3/14
Inventor 熊红凯唐欣
Owner SHANGHAI JIAO TONG UNIV
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