Due to rapid growth in image sizes, an alternate of numerically lossless coding named visually lossless coding is considered to reduce storage size and lower data transmission. In this paper, a lossy compression method on encrypted color image is introduced with undetectable quality loss and high compression ratio. The proposed method includes the Xinpeng Zhang lossy compression [1], Hierarchical Oriented Prediction (HOP)[2], Uniform Quantization, Negative Sign Removal, Concatenation of 7-bit data and Huffman Compression. The encrypted image is divided into rigid and elastic parts. The Xinpeng Zhang elastic compression is applied on elastic part and HOP is applied on rigid part. This method is applied on different test cases and the results were evaluated. The experimental evidences suggest that, the proposed method has better coding performance than the existing encrypted image compressions, with 9.645 % reductions in bit rate and the eye perception is visually lossless.

In recent years, secure information sharing has become a top requirement for many applications such as banking and military. Secret Sharing is an effective method to improve security of data. Secret Sharing helps to avoid storing data at a single point through dividing and distributing “shares” of secrets and recovering it later with no loss of original quality. This paper proposes a new Secret Sharing scheme for secure transmission of color images. The key features of this scheme are better visual quality of the recovered image with no pixel expansion, eliminating half toning of color images, eliminating the need for code book to decrypt images since reconstruction is done through XOR ing of all images and non-requirement of regeneration of shares for addition or deletion of users leading to less computational complexity. Besides these advantages, this scheme also helps to renew shares periodically and is highly beneficial in applications where data has to be stored securely in a database.

The marine researchers analyze the behaviors of fish in the sea by manually viewing the full video for their research activity. Searching events of interest from a video database is a time consuming and tedious process. Video summary refers to representing the whole video using few frames. The objective of this work is to design and develop a statistical video summarization to perform the automatic detection of events of interest in underwater video. In this proposed work, a video is partitioned into adjacent and non-overlapping datacubes. Then, the video frames are transformed into wavelet sub-bands and the standard deviation between two consecutive frames is computed. Pixels of interest in frames are identified using threshold values. Key frames are identified using Local Maxima and Local Minima. The proposed work effectively detects even the movement of small water bodies such as crabs which is not detected using the existing methods. Finally, this paper presents the experimental results of proposed method and existing methods in terms of metrics that measure the valid of the work.