With the proliferation of cameras in handheld devices that allows users to capture still images and videos, providing users with software tools to efficiently manage multimedia content has become essential. In many cases users desire to organize their personal media content using high-level semantic labels. In this paper we will describe low-complexity algorithms that can be used to derive semantic labels, such as "indoor/outdoor," "face/not face," and "motion/not motion" for mobile video sequences. We will also describe a method for summarizing mobile video sequences. We demonstrate the classification performance of the methods and their computational complexity using a typical processor used in many mobile terminals.
Recent advances in digital video coding tools have led to the introduction of the H.264 video coding standard, which promises increased visual quality and reduced bandwidth. In this paper, we
analyze and compare MPEG-2 and H.264 video compression methods. Although H.264 is similar to MPEG-2 in that both are hybrid coders
that use motion compensation, H.264 also includes advanced features such as improved entropy encoding, in-loop filtering of reference frames, flexible macroblock sizing, and multiple reference frame capability. Many experiments were performed to illustrate the coding gains of each feature in H.264 as well as to compare H.264 to MPEG-2. Quality was measured using two different objective video metrics: peak signal-to-noise ratio and the Structural Similarity Index. A variety of natural video test sequences were used with varying resolutions and data rates. TM5 and JM reference software were used to create MPEG-2 and H.264 compressed bitstreams. Results for all experiments show significant coding gain with H.264 versus MPEG-2 when compressing natural video sequences, especially at low data rates.