This paper considers a layered coding framework with a relaxed hierarchical structure, tailored to serve content at multiple quality levels, where a key challenge is the conflict between coding optimality at each layer and efficient use of storage and networking resources. The prevalent approach of storing and transmitting independent copies for each quality level, is highly wasteful in resources. The alternative of conventional scalable coding incurs the notorious “scalability penalty” at the enhancement layers, due to its rigid structure. The approaches pursued in this work involve a layered coding framework, wherein information common to one or more subsets of the quality levels is first extracted and transmitted, and then complemented by individual (quality level specific) bit streams. This framework ensures that no redundant or irrelevant information is sent to any decoder, enables achieving all intermediate operating points between the two extremes of conventional scalable coding versus independent coding, and hence mitigates the layered coding penalty. Joint design of common and individual layers ensures that all extracted common information is fully usable by the target decoders, as needed to approach optimality. Simulation results for practically important sources, confirm the superiority of the proposed framework.

中文翻译：

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量化器设计，用于分层和可扩展编码中的公共信息

本文考虑了一种具有宽松层次结构的分层编码框架，该框架旨在为多种质量级别的内容提供服务，其中主要的挑战是每层编码的优化与有效利用存储和网络资源之间的冲突。存储和传输每个质量级别的独立副本的普遍方法非常浪费资源。常规的可伸缩编码的替代方案由于其刚性结构而在增强层引起了臭名昭著的“可伸缩性代价”。在这项工作中追求的方法涉及一个分层的编码框架，其中首先提取和传输质量级别的一个或多个子集所共有的信息，然后再通过各个（特定于质量级别的）比特流进行补充。该框架确保没有冗余或不相关的信息被发送到任何解码器，实现传统可伸缩编码与独立编码的两个极端之间的所有中间工作点，从而减轻了分层编码的代价。公共层和单个层的联合设计可确保所有提取的公共信息完全可被目标解码器使用，以达到最佳化的需要。实际重要来源的仿真结果证实了所提出框架的优越性。公共层和单个层的联合设计可确保所有提取的公共信息完全可被目标解码器使用，以达到最佳化的需要。实际重要来源的仿真结果证实了所提出框架的优越性。公共层和单个层的联合设计可确保所有提取的公共信息完全可被目标解码器使用，以达到最佳化的需要。实际重要来源的仿真结果证实了所提出框架的优越性。