The Scalable extension of the High Efficiency Video Coding (known as SHVC) combines the high compression efficiency with the possibility of encoding different resolutions of the same encoded video in a single bitstream. However, this is accompanied with a high computational complexity. In this paper, we propose an effective coding unit (CU) size decision method by restricting the CU depth range to reduce the encoding time for quality scalability in SHVC. Since the optimal depth level in the enhancement layer (EL) is highly correlated to that in the base layer (BL), we can determine the CU depth range in the EL according to the depth of the co-located CU in the BL. Based on the high correlation between the current CU and its spatio-temporal neighboring CUs, the proposed method skips some specific depth levels which are rarely used in the previous frame and neighboring CUs to further reduce the computational complexity. Experimental results demonstrate that the proposed method can efficiently reduce computational complexity while maintaining similar rate distortion (RD) performance as the original SHVC encoder.

This paper presents a condensed semantic tree model for representing image category. For a specific application area, a semantic concept space is defined. According to the annotation for an image, a real-value semantic vector is gained that describes the content of it. In order to represent image category, condensed semantic tree model is introduced. It is a triple level structure. The bottom level is a semantic concept mask, which selects those concepts relevant to semantic category. The middle level is composed of three semantic modules, which extract high-level semantic of an image. The top level analyzes the probability that an image is belong to a specific image category. Every semantic category has different model configuration. The experimental results illustrate that the effectiveness of the proposed condensed semantic tree model is good.

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As a patient and recipient of a blood transfusion, it is important for him or her to receive the safest blood possible. Information about the donated blood should be under the track to guarantee the quality of the blood source. In this paper, we present a RFID-based blood information management system that aims at ensuring the quality of the blood and increasing the efficiency of operation management. In this system, the fingerprint sensor is adopted to enable the process of identifying blood donor more reliable and credible and RFID tag is used to make the management more convenient. In addition, GPRS is applied in this system so that real-time data can be transmitted between the bloodmobile and blood center through wireless Internet.

In order to further improve fractional-pel interpolation image quality of video sequence with different resolutions and reduce algorithm complexity, the fractional-pel interpolation algorithm based on adaptive filter (AF_FIA) is proposed. This algorithm adaptively selects the interpolation filters with different orders according to the three video sequence regions with different resolutions; in the three video sequence regions with different resolutions, the high-order interpolation filter is replaced by low-order interpolation filter according to the correlation between pixels to realize the adaptive selection of filter. The complexity analysis results show that compared with other algorithms, this algorithm reduces space complexity and computation complexity, thus reducing the storage access and coding time. The simulation results indicate that compared with other algorithms, this algorithm has good coding performance and robustness for video sequences with different resolutions.