Zoomable Video Research Articles

Wireless Multicast for Zoomable Video Streaming

Zoomable video streaming refers to a new class of interactive video applications, where users can zoom into a video stream to view a selected region of interest in higher resolutions and pan around to move the region of interest. The zoom and pan effects are typically achieved by breaking the source video into a grid of independently decodable tiles. Streaming the tiles to a set of heterogeneous users using broadcast is challenging, as users have different link rates and different regions of interest at different resolution levels. In this article, we consider the following problem: Given the subset of tiles that each user requested, the link rate of each user, and the available time slots, at which resolution should each tile be sent, to maximize the overall video quality received by all users. We design an efficient algorithm to solve this problem and evaluate the solution on a testbed using 10 mobile devices. Our method is able to achieve up to 12dB improvements over other heuristic methods.

Scalable storyboards in handheld devices: applications and evaluation metrics

Summaries are an essential component of video retrieval and browsing systems. Most research in video summarization has focused on content analysis to obtain compact yet comprehensive representations of video items. However, important aspects such as how they can be effectively integrated in mobile interfaces and how to predict the quality and usability of the summaries have not been investigated. Conventional summaries are limited to a single instance with certain length (i.e. a single scale). In contrast, scalable summaries target representations with multiple scales, that is, a set of summaries with increasing length in which longer summaries include more information about the video. Thus, scalability provides high flexibility that can be exploited in devices such as smartphones or tablets to provide versions of the summary adapted to the limited visualization area. In this paper, we explore the application of scalable storyboards to summary adaptation and zoomable video navigation in handheld devices. By introducing a new adaptation dimension related with the summarization scale, we can formulate navigation and adaptation in a two-dimensional adaptation space, where different navigation actions modify the trajectory in that space. We also describe the challenges to evaluate scalable summaries and some usability issues that arise from having multiple scales, proposing some objective metrics that can provide useful insight about their potential quality and usability without requiring very costly user studies. Experimental results show a reasonable agreement with the trends shown in subjective evaluations. Experiments also show that content-based scalable storyboards are less redundant and useful than the content-blind baselines.

Adaptive encoding of zoomable video streams based on user access pattern

Zoomable video allows users to selectively zoom and pan into regions of interest within the video for viewing at higher resolutions. Such interaction requires dynamic cropping of RoIs on the source video. We have previously explored two different ways of encoding and transmitting video to support dynamic RoI cropping: (i) Monolithic streaming uses a standard video encoder to encode the video. When an RoI is requested, the bits belonging to the RoI along with other bits required to decode the RoIs (due to encoding dependencies) are transmitted. (ii) Tile streaming divides regions in the standard video into rectangular tiles that are encoded independently. The tiles that intersect with a requested RoI are transmitted. In this paper, we consider how the bandwidth needed to transmit the RoIs can be reduced by carefully encoding the source video for each of the two encoding schemes. The goal is to support bandwidth efficient compressed domain RoI cropping in the context of virtual zoom and pan by tuning encoder parameters. Our key idea is to exploit user access patterns to the RoIs, and encode different regions of the video with different encoding parameters based on the popularity of the region. We show that our encoding method can reduce the expected bandwidth by up to 43% in the test video sequence which we have used.