High-speed image compression method and device based on optimally quantized wavelet sub-band interlacing

Abstract

The invention relates to a high-speed image compression method based on optimal wavelet sub-band interlacing, which comprises the following steps of: acquiring CCD (Charge Coupled Device) camera data and carrying out 3-level wavelet transform on the CCD camera data; converting parallel sub-band data subjected to the wavelet transform into a sub-band interlaced serial data stream and carrying out optimal quantization; predicating quantized lowest frequency wavelet sub-band data by using a JPEG-LS (Joint Photographic Experts Group-Lossless Standard) algorithm; and carrying out run-length coding and exponential-Golomb coding on the quantized and predicated data to obtain a final code stream of an acquired image. The invention also provides a compression device. According to the image compression method and device, the whole process of image data acquisition, coding and transmission can be achieved by only using a single-chip field programmable logic device, the compression ratio can be adjusted by a quantization step and the throughput of single-channel data can reach 350 MSPS (Million Samples Per Second).

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a method for encoding images at high speed and an engineering realization device thereof. Background technique [0002] With the advancement of image sensors and electronic technology, the resolution and sampling rate of CCD cameras used in aerial surveys and other fields have been increasing, resulting in a large amount of image data. How to effectively transmit and store these data has become the focus of people's increasing attention. Efficient compression coding technology is an effective way to solve this problem. [0003] Classical coding methods mainly include predictive coding, transform coding and statistical coding. Predictive coding is to obtain the predicted value according to the statistical characteristics of the data, and then transmit the difference signal between the image pixel and its predicted value to reduce the bit rate of its transmission. Transform coding ...

AI Technical Summary

Problems solved by technology

This algorithm is also composed of a wavelet transform module and a bit encoding module. Its structure is simpler than JPEG2000 and it is easy to implement. However, the coefficients after wavelet transform need to be read out in the order of the spanning tree and sent to each bit plane encoder for bit plane encoding. Coding, therefore, the data after wavelet transformation must be cached for scan coding, and the speed is still not likely to be greatly improved

Images