Image Insertion Using Depth- and Topology- Constrained Minimum Spanning Tree for Compressed Image Sets

As an important paradigm of image set management, image insertion refers to adding new photos to an existing compressed image set. Recently, several algorithms have made significant progress in image insertion. However, due to complexity image relationships, coding performance still remains to rise. To address the issue, in this paper a high-coding-efficiency image insertion algorithm is proposed for compressed image sets. In order to maximize coding performance, it is necessary for each new picture to thoroughly exploit the correlations between it and all the other images. To be specific, in our proposed approach images are first divided into two kinds: to-be-inserted images and compressed images, where the former includes new photographs and the latter composes that compressed image set. Second, a depth- and topology-constrained minimum spanning tree (DTCMST) heuristics is also proposed, to fully investigate the relationships not only between to-be-inserted images and compressed images but among different to-be-inserted images. With the generated DTCMST, the depth requirement and the topology structure of the existing compressed image set can be satisfied and kept unchanged, respectively. Finally, after the encoding of every to-be-inserted image by using its assigned parent vertex from the DTCMST as its prediction reference, a new compressed image is eventually established. Compared with state-of-the-art methods, experimental results show that average bit rate saving and Bjontegaard delta peak signal-to-noise ratio enhancement are achieved up to 5.1 % and 0.41 dB by using our proposed algorithm, respectively, with the similar computational complexity.