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Quantization method of dictionary learning-based image compression system

A technology of dictionary learning and image compression, applied in image communication, image coding, image data processing, etc., can solve problems such as poor quantization performance

Active Publication Date: 2017-05-10
TSINGHUA UNIV +1
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Problems solved by technology

However, the quantization performance of this method is poor when the data have different weights, especially when the weights of data-dense regions are small

Method used

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  • Quantization method of dictionary learning-based image compression system
  • Quantization method of dictionary learning-based image compression system
  • Quantization method of dictionary learning-based image compression system

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Embodiment Construction

[0091] Specific requirements:

[0092] use Figure 4 As shown, 10 natural images with a size of 512×512 are used to train the learning dictionary, and the learning dictionary is used to Figure 5 The shown natural image of size 512 × 512 was compressed using a "dictionary learning-based image compression system", requiring its reconstructed image PSNR to be at least 50dB.

[0093] Offline part:

[0094] Using the K-SVD dictionary learning algorithm to Figure 4 The 10 pieces of natural image data with a size of 512×512 are shown as training samples, and a learning dictionary D with a size of 64×512 is trained.

[0095] Online section:

[0096] On the encoding side, follow the steps below to Figure 5 The shown natural image data to be compressed is converted into a bit stream suitable for channel transmission:

[0097] Step (1), initialization:

[0098] Stored in the learning dictionary D,

[0099] Set the reconstructed image minimum The given value is 50dB,

[0100...

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Abstract

The invention relates a quantization method of a dictionary learning-based image compression system and belongs to the image compression technology filed in multimedia communication. According to the method of the invention, zero coefficients are removed from a coefficient matrix; nonzero coefficient values are sorted; a nonzero coefficient sequence is truncated through using an estimated truncation coefficient percentage; a reserved nonzero coefficient sequence is normalized; a uniform quantization method is adopted to divide the processed nonzero coefficient sequence into equal subintervals; K-means clustering quantization is carried out independently in each subinterval; in the iteration process of the K-means clustering quantization, the mean value of all elements in each category is adopted as a new clustering center of the category; after an iteration termination condition is satisfied, all nonzero coefficients in each category are quantified into corresponding clustering center values; the PSNR (Peak Signal to Noise Ratio) of a reconstructed image is calculated and is compared with the set minimum PSNR given value of the reconstructed image; the truncation coefficient percentage is adjusted; and the above operation is repeated until the calculated PSNR value of the reconstructed image is not lower than the minimum PSNR given value of the reconstructed image. Compared with a quantization method according to which uniform quantization or K-means clustering quantization is used independently, the dictionary learning-based quantization method of the image compression system is advantageous in optimal quantification performance.

Description

technical field [0001] The invention belongs to the technical field of image compression in multimedia communication. Background technique [0002] Image compression has always been one of the classic problems in the field of image processing, aiming at removing redundancy and correlation in images to achieve efficient transmission or storage of image data. In order to meet the growing application requirements and the rapid development of multimedia communication technology, multimedia files such as videos and images continue to break through in the direction of higher precision and higher resolution. posed serious challenges. In order to effectively solve this problem, image compression has always been a research hotspot in the field of image processing. [0003] Image compression methods can be divided into two categories: lossless compression and lossy compression. The former requires the decoder to restore the original image without distortion, while the latter allows...

Claims

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Application Information

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IPC IPC(8): H04N19/124H04N19/13H04N19/94H04N19/91G06K9/00G06K9/62G06T9/00
CPCH04N19/124H04N19/13G06T9/008G06V40/16G06F18/2136G06F18/28G06F18/23
Inventor 陶晓明王隽徐迈刘喜佳葛宁陆建华
Owner TSINGHUA UNIV
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