Rate Control Method For Satellite Image Compression

With the rapid development of remote sensing technology,high-resolution earth observation satellites have become one of the important strategic resources of the country.Since the image acquired by the satellite is different from that of natural scenes,the satellite image has the characteristics of large width,high precision and complex background.Under the extreme satellite resource environment and limited satelliteearth transmission bandwidth,the unprocessed satellite image data will cause great challenges to on-board memories and processors,so an image compression system needs to be introduced onto the satellite.In order to meet the demand of satellite-earth transmission,satellite images usually need to adopt a larger compression rate than natural scene images.The traditional image compression method adopts the same compression scheme for all minimum encoding units,which is difficult to guarantee the image quality under low bit rate.At the same time,processing large-width satellite images increases the time required for the compression process,which is difficult to meet the demand of low latency transmission of satellite images.The application of satellite image is not about viewing,but about the understanding and analysis of image contents.Although the satellite image has a large width and a complex background,some regions of the image still are valuable for extraction and analysis,which are called salient regions of the satellite image.And the saliency detection on satellite images enables people to minimize irrelevant information and extract important elements from the source image.The quality and time optimization in image compression usually depends on the improvement of the bit-rate control process.Allocating more bit rate on salient region while reducing redundant compression operations for non-salient region in the process of bit-rate control,can effectively improve the compression quality of satellite image and reduce the time complexity of bit-rate control.At present,the saliency detection method of image is oriented to natural scene images,in which the background is simple and the salient region is single and centralized.Therefore,it is not suitable for satellite images.At the same time,each image compression method adopts the same bit-rate control process for its minimum encoding unit,which generally does not consider the different importance of the content of images,and does not conduct the on-demand allocation to the bit rate,so the overall efficiency of image compression is relatively low.Aiming at the shortcomings of the traditional rate control method,this paper provides a rate control method for satellite image compression based on the JPEG2000 image compression standard.By the saliency detection of satellite image,this method can improve and optimize the quality and compression time of the rate control process.The method,according to the characteristics of satellite images,firstly screens the most suitable method for satellite images in the mainstream image saliency detection ones,and then processes this suitable method;after that,the obtained saliency image is used to the rate control process for optimizing both compression quality and time—compression quality optimization is applied to the quantization phase in the rate control process,in which saliency detection is used to perform weighted allocation of quantization step to achieve the purpose of the on-demand distribution of rate according to image content importance;the compression time optimization acts on the entropy coding stage,in which the saliency detection is used to allocate the coding primitives of each pixel,avoiding the scanning process of the three coding passes,skipping the non-salient pixels,and reducing the rate control time redundancy of the process.In this paper,the effectiveness of the proposed method is tested by three factors: rate control accuracy,subjective and objective quality,and algorithm time consumption.The experimental results show that the proposed method has good rate control accuracy,and the PSNR of the salient regions in the image is about 1.82 dB higher than that of the original JPEG2000 at low bit rate.The algorithm takes about 23% less time than that of existing methods.