Finally, we define and optimize our objective function from the blocking artifact density functions in order to select a bitrate with minimum perceptual blockiness and file size for each frame. Based on the impact metric, we generate a blocking artifact density functions for the available bitrates, on the whole video.
The proposed method consists of the following steps: First, we define a simple and fast impact metric in order to identify the blocking artifacts in each frame of multiple videos, encoded at different bitrates. We propose a new direction of constructing mixed bitrate video based on content-based image analysis on each video frame, which was posed as a problem of pre-analysis for the final video encoding step. The trade-off between these artifacts and bi-trate can be improved by adaptively selecting frames from a set of video copies encoded at different bitrates, prior to actual video encoding. Our evaluation using the real player implementation shows that BETA improves video quality by up to 20%, reduces number of stalls by 20%-100% in nearly 80% of cases, and cuts down wasted bandwidth by 22%-100%.īlocking artifacts, commonly introduced during video encoding, are one of the major causes of reduced perceptual video quality. BETA can work with any adaptation algorithm or HAS player to significantly improve robustness and efficiency in dynamic network environments and for low-latency streams, as well as to dramatically reduce content storage and encoding infrastructure requirements. We define a new HAS-oriented transmission order of video frames within segments that facilitates decodability of partial frames and paves the way for changing the paradigm from discrete to continuous bitrate ladders for HAS. We propose BETA- Bandwidth-Efficient Temporal Adaptation, an agile approach that allows HAS players to refine the quality level within video segments on the fly, according to the actual bandwidth conditions experienced while downloading each segment. This approach is not robust to bandwidth fluctuation at small time scales, which can consequently lead to stalls, bandwidth waste, and unstable quality, mainly due to the inability to mitigate significant bandwidth reduction during the segment download. Conventional approach to adaptation is to make a decision on the next video segment quality based on hysteresis of prior throughput measurements. However, effective adaptation that minimizes stalls and start-up time while maximizing quality and stability remains elusive, especially when available bandwidth is variable or multiple players compete for the bottleneck capacity. To cope with diverse network conditions, HTTP Adaptive Streaming (HAS) enables video players to dynamically change the video quality throughout the video stream.
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