HBTC Database: A Brief Review on Halftone based Block Truncation Coding (HBTC) Images Halftone based block truncation coding images (HBTC) are the improved version of BTC images which can offer superior representation and enhanced image quality. The application of the HBTC images range from image compression and retrieval, indexing, reconstruction, classification and so on. In this paper, a brief introduction to various HBTC images is presented and their evaluation is carried out in terms of image quality and computational demand. Further, the application prospective of the HBTC images are also provided. The developed HBTC database comprises of 30K images comprising of the five types of HBTC images in which three categories are constructed based on digital halftoning and two types are based on digital multitoning. The database would be very useful to carry out deep learning research and various image processing tasks. The database along with its source code will be made open source for the research and academic purpose.

HighPayload DataHiding Method for AMBTC Decompressed Images Data hiding is the art of embedding data into a cover image without any perceptual distortion of the cover image. Moreover, data hiding is a very crucial research topic in information security because it can be used for various applications. In this study, we proposed a highcapacity datahiding scheme for absolute moment block truncation coding (AMBTC) decompressed images. We statistically analyzed the composition of the secret data string and developed a unique encoding and decoding dictionary search for adjusting pixel values. The dictionary was used in the embedding and extraction stages. The dictionary provides high datahiding capacity because the secret data was compressed using dictionarybased coding. The experimental results of this study reveal that the proposed scheme is better than the existing schemes, with respect to the datahiding capacity and visual quality.