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A low bit-rate SOC-based reversible data hiding algorithm by using new encoding strategies

机译：通过使用新的编码策略的低比特率基于SOC的可逆数据隐藏算法

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摘要

Search order coding (SOC) benefits a lot from the correlation of neighboring blocks for vector quantization (VQ)-compressed images. SOC selects a number of different indices in its search path as candidate search order codes. In this work, we present a low bit-rate SOC-based reversible data hiding algorithm benefiting from novel encoding strategies which exploit the information of the placeholders. The blocks are classified into two categories by the placeholders, where different encoding strategies are designed, respectively. Firstly, for the block in smooth region, the placeholders in its neighborhood are employed to compress the VQ index. Secondly, for the block in complex region, SOC is employed to compress the VQ index. Finally, for the blocks that cannot be processed by its placeholders and SOC, an effective prediction method named accurate gradient selective prediction (AGSP) and Huffman coding are introduced. After encoding phase, the size of the output bit stream is reduced so that space is saved for data embedding. Experiment results show that our proposed method outperforms other state-of-the-art SOC-based algorithms in bit rate and embedding capacity.
机译：搜索顺序编码（SOC）受益于矢量量化（VQ）压缩图像的相邻块的相关性。 SOC在其搜索路径中选择许多不同的索引作为候选搜索顺序代码。在这项工作中，我们提出了一种基于低比特率SOC的可逆数据隐藏算法，该算法受益于利用占位符信息的新颖编码策略。占位符将块分为两类，分别设计了不同的编码策略。首先，对于光滑区域中的块，使用其附近的占位符来压缩VQ索引。其次，对于复杂区域中的块，采用SOC来压缩VQ指数。最后，针对不能由其占位符和SOC处理的块，引入了一种有效的预测方法，称为精确梯度选择性预测（AGSP）和霍夫曼编码。在编码阶段之后，减小了输出位流的大小，从而节省了用于数据嵌入的空间。实验结果表明，我们提出的方法在比特率和嵌入能力方面优于其他基于SOC的最新算法。

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