In many real engineering applications, the distribution of training scenarios (source domain) and the distribution of testing scenarios (target domain) is different, thus the classification performance decreases sharply when simply applying the classifier trained in source domain directly to the target domain. At present, most of the existing domain adaptation methods are based on the probability-inference. For the problem of domain adaptation image classification, a collaborative representation based unsupervised method was proposed from the view of image representation. Firstly, all of the source samples were taken as the dictionary. Secondly, the three target samples closest to the target sample in the target domain were exploited to robustly represent the local-neighbor geometrical information. Thirdly, the target sample was encoded by combining the dictionary and the local-neighbor information. Finally, the classification was completed by using the nearest classifier. Since the collaborative representations have stronger robustness and discriminative ability by absorbing the target local-neighbor information, the classification method based on the new representations has better classification performance. The experimental results on the domain adaptation dataset confirm the effectiveness of the proposed method.

Sample Adaptive Offset (SAO) is a time-consuming part of in-loop filter in the second generation of Audio Video coding Standard (AVS2) and High Efficiency Video Coding (HEVC) standard. Aiming at the problem that existing SAO algorithms had large amounts of computation and high complexity, an improved fast rate-distortion algorithm was proposed. In this new method, the original defined table of the offset values and its binary bit string to be written into the code stream were modified by analyzing the relationship between the different offset values of each class in the edge mode and its change of the rate-distortion, so that an early termination condition was set to quickly find the best offset value for the current SAO unit without calculating the rate-distortion cost of each offset. The experimental results show that, compared with the calculation results in AVS2, the proposed algorithm reduces not only the calculation amounts but also the number of cycles by 75% to find the best offset values and the operating time of in-loop filter by 33%, which effectively lowers the complexity of the calculation in ensuring the rate-distortion of image barely changed.