Abstract
In an error-prone transmission environment, error concealment is an effective technique to reconstruct the damaged visual content. Due to large variations of image characteristics, different concealment approaches are necessary to accommodate the different nature of the lost image content. In this paper, we address this issue and propose using classification to integrate the state-of-the-art error concealment techniques. The proposed approach takes advantage of multiple concealment algorithms and adaptively selects the suitable algorithm for each damaged image area. With growing awareness that the design of sender and receiver systems should be jointly considered for efficient and reliable multimedia communications, we proposed a set of classification-based block concealment schemes, including receiver-side classification, sender-side attachment, and sender-side embedding. Our experimental results provide extensive performance comparisons and demonstrate that the proposed classification-based error concealment approaches outperform the conventional approaches.
Highlights
Due to the various kinds of distortion and failures, part of a compressed image or video can be damaged or lost during transmission or storage
This is because some state-of-the-art approaches have rather high computation demand, and classification allows the computation power to be spent more strategically by performing expensive computations only when they are likely to offer a substantial gain in the concealment quality
SVMlight is an implementation of support vector machine (SVM) based on the optimization algorithm in [19]
Summary
Due to the various kinds of distortion and failures, part of a compressed image or video can be damaged or lost during transmission or storage. Through a benchmarking effort on the existing error concealment approaches, we have observed that different approaches are suitable for different image characteristics of a corrupted block and its surroundings, and none of the existing approaches is an alltime champion This motivates us to explore a classificationbased concealment approach that can combine the better performance of two state-of-the-art approaches in the literature. To provide more proactive protection and further exploit the knowledge from the original, uncorrupted image, a few recent works in the literature [9,10,11] have jointly considered the design of sender and receiver systems to facilitate error concealment We explore this sender-driven perspective for our classification-based concealment framework by obtaining a small amount of classification data on the sender side.
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