Abstract
It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, this paper proposes a joint framework based on neural network and zoom compression. The framework first encodes the incoming PNG or JPEG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next we import the intermediate state image into the zooming compressor and re-pressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system can not feel the change of the effect.
Highlights
In the past few years, digital image compression has always been an important field in the computer field
Our compression is a combination of neural network image compression and image scaling compression method
[3] is pure area reconstruction images using neural network, the size is not limited to 32 × 32, and our goal is to be on the premise of existing methods, modify the unnecessary data, make it and we provide image scaling compression method combination in the form of compression and compression method of neural network in any size of image compression ratio has a strong advantage
Summary
In the past few years, digital image compression has always been an important field in the computer field. A simple black and white photo, for example, if the 512 × 512 sampling lattice, said that the binary image data volume for 512 × 512 × 8 = 2048 Kbit = 2 Mbit = 256 KB, and medical image processing and other scientific research and application of image grayscale quantitative available to more than 12 bits, so the data needs to be bigger and bigger, not to mention the remote sensing images such as SAR images could be far more than our common image size, for the image data storage, transmission, processing is a big challenge. Data volume is far beyond the storage and processing capacity of the computer, and the transmission rate of the current communication channel is less than, so compression naturally picked up this difficult task. An example of JPEG secondary compression is mentioned in [8]. It presents a method for detecting double JPEG compression and the maximum likelihood estimation of the principal mass factor.
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