Abstract
Due to the limited storage space of spacecraft and downlink bandwidth in the data delivery during planetary exploration, an efficient way for image compression onboard is essential to reduce the volume of acquired data. Applicable for planetary images, this study proposes a perceptual adaptive quantization technique based on Convolutional Neural Network (CNN) and High Efficiency Video Coding (HEVC). This technique is used for bitrate reduction while maintaining the subjective visual quality. The proposed algorithm adaptively determines the Coding Tree Unit (CTU) level Quantization Parameter (QP) values in HEVC intra-coding using the high-level features extracted by CNN. A modified model based on the residual network is exploited to extract the saliency map for a given image automatically. Furthermore, based on the saliency map, a CTU level QP adjustment technique combining global saliency contrast and local saliency perception is exploited to realize a flexible and adaptive bit allocation. Several quantitative performance metrics that efficiently correlate with human perception are used for evaluating image quality. The experimental results reveal that the proposed algorithm achieves better visual quality along with a maximum of 7.17% reduction in the bitrate as compared to the standard HEVC coding.
Highlights
The image data collected during the planetary exploration has significant research value for scientists to analyze the special geographical and geological environment
A visual saliency guided perceptual image compression method is suggested to achieve better subjective High Efficiency Video Coding (HEVC) intra-coding performance for the planetary images. This is the first effort toward developing a perceptual adaptive quantification technique for onboard planetary image compression
The proposed algorithm utilizes a modified model based on pre-trained ResNet50 to extract the saliency map, which indicates the preference of texture features in Convolutional Neural Network (CNN)
Summary
The image data collected during the planetary exploration has significant research value for scientists to analyze the special geographical and geological environment. To improve the coding quality of salient regions for each frame, Sun et al [17] proposed an adaptive rate control method based on the fusion saliency map consisting of static and dynamic salient feature computed from the deep CNN model and motion target segmentation algorithms. To facilitate the planetary image compression process using a visual saliencyguided perceptual technique, a perceptual adaptive quantization method based on CNN and HEVC intra-coding has been proposed. The primary features include flexible recursive quadtree structure for block structure partitioning, multiple intra-prediction modes, Syntax-Based Context-Adaptive Binary Arithmetic Coding (SBAC) and Sample-Adaptive Offset (SAO) filtering It has a design objective of better video compression, the Main Still Picture (MSP) profile of HEVC can be efficiently utilized to compress still images configured with the intra-coding pattern. The proposed work focuses on the improvement of HEVC intra-coding
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