This paper presents a novel peer-to-peer protocol to efficiently distribute virtual machine images in a datacenter. A primary idea of it is to improve the performance of peer-to-peer content delivery by employing deduplication to take advantage of similarity both among and within VM images in cloud datacenters. The efficacy of the proposed scheme is validated through an evaluation that demonstrates substantial performance gains.

None of the existing color constancy algorithms can be considered universal. Furthermore, they use all the image pixels, although actually not all of the pixels are effective in illumination estimation. Consequently, how to select a proper color constancy algorithm from existing algorithms and how to select effective(or useful) pixels from an image are two most important problems for natural images color constancy. In this paper, a novel Color Constancy method using Effective Regions (CCER) is proposed, which consists of the proper algorithm selection and effective regions selection. For a given image, the most proper algorithm is selected according to its Weilbull distribution while its effective regions are chosen based on image similarity. The experiments show promising results compared with the state-of-the-art methods.

Computational color constancy is a classical problem in computer vision. It is an under-constrained problem, which can be solved based on some constraint. Existing algorithms can be divided into two groups: physics-based algorithms and statistics-based approaches. In this paper, we propose a new hypothesis that the images generated under a same illumination have some similar features. Based on this hypothesis, a novel statistics-based color constancy algorithm is given and a new similarity function between images is also defined. The experimental results show that our algorithm is effective and it is more important that the dimension of the features in our algorithm is much lower than many former statistics-based algorithms.

We propose a method to retrieve similar and duplicate images from a JPEG (Joint Photographic Image Group) image database. Similarity level is decided based on the DCT (Discrete Cosine Transform) coefficients signs. The method is simple and fast because it uses the DCT coefficients signs as features, which can be obtained directly after partial decoding of JPEG bitstream. The method is robust to JPEG compression, in which similarity level of duplicate images, i.e., images that are compressed from the same original images with different compression ratios, is not disguised due to JPEG compression. Simulation results showed the superiority of the method compared to previous methods in terms of computational complexity and robustness to JPEG compression.