Abstract
Absolute moment block truncated coding (AMBTC) is a lossy image compression technique aiming at low computational cost, and has been widely studied. Previous studies have investigated the performance improvement of AMBTC; however, they often over describe the details of image blocks during encoding, causing an increase in bitrate. In this paper, we propose an efficient method to improve the compression performance by classifying image blocks into flat, smooth, and complex blocks according to their complexity. Flat blocks are encoded by their block means, while smooth blocks are encoded by a pair of adjusted quantized values and an index pointing to one of the k representative bitmaps. Complex blocks are encoded by three quantized values and a ternary map obtained by a clustering algorithm. Ternary indicators are used to specify the encoding cases. In our method, the details of most blocks can be retained without significantly increasing the bitrate. Experimental results show that, compared with prior works, the proposed method achieves higher image quality at a better compression ratio for all of the test images.
Highlights
With the rapid development of imaging technology, digital images are perhaps the most widely used media of the Internet
Because digital images themselves contain significant amounts of spatial redundancy, an efficient lossy image compression technique is required for lower storage requirement and faster transmission
Xiang et al [16] in 2019 proposed a dynamic multi-grouping scheme for absolute moment block truncation coding (AMBTC) focusing on improving the reconstructed image quality and reducing the bitrate
Summary
With the rapid development of imaging technology, digital images are perhaps the most widely used media of the Internet. The Joint Photographic Experts Group (JPEG) [1,2], vector quantization (VQ) [3,4], and block truncation coding (BTC) [5,6] are well-known lossy compression methods and have been extensively investigated in the literature Among these techniques, BTC requires significantly less computation cost than others while offering acceptable image quality. To improve the compression efficiency of the AMBTC method, several approaches, including bitmap omission [15], block classification [16,17], and quantized value adjustment [18], are adopted to lower the bitrate while maintaining the image quality. Xiang et al [16] in 2019 proposed a dynamic multi-grouping scheme for AMBTC focusing on improving the reconstructed image quality and reducing the bitrate Their method partitions an image into non-overlapping blocks.
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