论文标题
深度压缩图像潜在图像的RD优化TRIT平面编码
RD-Optimized Trit-Plane Coding of Deep Compressed Image Latent Tensors
论文作者
论文摘要
DPICT是支持精细粒状可扩展性的第一个基于学习的图像编解码器。在本文中，我们描述了如何有效地实现DPICT的两个关键组成部分：TRIT平面切片和速率优化（RD优化）编码。在DPICT中，我们将图像转换为潜在张量，代表三元数字（TRITS）的张量，并以降低显着性顺序对TRIT进行编码。对于熵编码，有必要计算每个TRIT的概率，这需要编码器和解码器的高时间复杂性。为了降低复杂性，我们为概率开发了并行计算方案，该方案用伪代码详细描述。此外，我们将DPICT中的Trit平面切片与替代的位平面切片进行了比较。实验结果表明，平行计算可以显着降低时间复杂性，而TRIT平面切片比比特平面切片提供了更好的RD性能。
DPICT is the first learning-based image codec supporting fine granular scalability. In this paper, we describe how to implement two key components of DPICT efficiently: trit-plane slicing and rate-distortion-optimized (RD-optimized) coding. In DPICT, we transform an image into a latent tensor, represent the tensor in ternary digits (trits), and encode the trits in the decreasing order of significance. For entropy encoding, it is necessary to compute the probability of each trit, which demands high time complexity in both the encoder and the decoder. To reduce the complexity, we develop a parallel computing scheme for the probabilities, which is described in detail with pseudo-codes. Moreover, we compare the trit-plane slicing in DPICT with the alternative bit-plane slicing. Experimental results show that the time complexity is reduced significantly by the parallel computing and that the trit-plane slicing provides better RD performances than the bit-plane slicing.
