Abstract
The JPEG2000 image compression standard is ideal for processing remote sensing images. However, its algorithm is complex and it requires large amounts of memory, making it difficult to adapt to the limited transmission and storage resources necessary for remote sensing images. In the present study, an improved rate control algorithm for remote sensing images is proposed. The required coded blocks are sorted downward according to their numbers of bit planes prior to entropy coding. An adaptive threshold computed from the combination of the minimum number of bit planes, along with the minimum rate-distortion slope and the compression ratio, is used to truncate passes of each code block during Tier-1 encoding. This routine avoids the encoding of all code passes and improves the coding efficiency. The simulation results show that the computational cost and working buffer memory size of the proposed algorithm reach only 18.13 and 7.81%, respectively, of the same parameters in the postcompression rate distortion algorithm, while the peak signal-to-noise ratio across the images remains almost the same. The proposed algorithm not only greatly reduces the code complexity and buffer requirements but also maintains the image quality.
Highlights
A remote sensing image can capture abundant information about geological structures, landforms, and other natural features; it has been widely applied in meteorology, lunar exploration engineering, environmental monitoring, resource exploration, and other fields
The proposed rate control algorithm is tested using eight test images that are selected randomly from 30 remote sensing images of different sizes. It is implemented on the Jasper software platform [13], which is defined in Part 5 of the JPEG2000 standard
This paper presents an improved rate control algorithm for remote sensing images
Summary
A remote sensing image can capture abundant information about geological structures, landforms, and other natural features; it has been widely applied in meteorology, lunar exploration engineering, environmental monitoring, resource exploration, and other fields. The JPEG2000 algorithm has excellent compression performance, its standard rate control algorithm is highly complex, limiting its application This complexity derives from embedded block coding with optimized truncation (EBCOT) [4]. The main characteristic of this new control algorithm is its mathematical model of the wavelet coefficients due to the distributive properties of the coefficients in each frequency band after the image wavelet distribution This model can find the real contribution code rate of every encoded block prior to encoding. In a previous study of image recovery, a rate-distortion (R-D) estimation for fast JPEG2000 compression at low bit rates was developed [9] This estimation utilizes the contexts of the wavelet coefficients, which are typically calculated during Tier-1 encoding; this context generation is a major contributor to the computational complexity of JPEG2000 compression.
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