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The cost of uncompressing (decoding) data can be prohibitive in certain real-time applications,
for example when predicting using compressed deep learning models. In many scenarios, it is
acceptable to sacrifice to some extent on compression in the interest of fast decoding. In this
work, we are interested in finding the prefix tree having the best decode time under the constraint
that the code length does not exceed a certain threshold for a natural class of algorithms under

Given a dynamic set K of k strings of total length n whose characters are drawn from an alphabet of size σ, a keyword dictionary is a data structure built on K that provides locate, prefix search, and update operations on K. Under the assumption that α = w / lg σ characters fit into a single machine word w, we propose a keyword dictionary that represents K in n lg σ + O(k lg n) bits of space, supporting all operations in O(m / α + lg α) expected time on an input string of length m in the word RAM model.

Motivated by the ever-increasing demands for limited communication bandwidth and low-power consumption, we propose a new methodology, named joint Variational Autoencoders with Bernoulli mixture models (VAB), for performing clustering in the compressed data domain. The idea is to reduce the data dimension by Variational Autoencoders (VAEs) and group data representations by Bernoulli mixture models (BMMs).

In this paper, the improvement of the cascaded prediction method was presented. The prediction method with backward adaptation and extended Ordinary Least Square (OLS+) was presented. An own approach to implementation of the effective context-dependent constant component removal block was used. Also the improved adaptive arithmetic coder with short, medium and long-term adaptation was used and the experiment was carried out comparing the results with other known lossless audio coders against which our method obtained the best efficiency.

The just noticeable distortion (JND) has been widely applied in perceptual image/video compression. Yet, the existing JND estimation models are not accurate enough, which results in the degradation of perceptual quality. In this paper, we propose a JND compensation based perceptual video coding (PVC) scheme to compress videos with better perceptual quality. Specifically, a block-level JND estimation model is proposed at first, which leads to this model can be employed in variable block-sizes based video coding directly.

With the rapid development of Internet, short videos draw more and more attentions nowa- days. Due to the small scale of short videos, image-level coding scheme can be applied to improve compression efficiency. In this paper, we propose a densely connected unit based loop filter for short video coding in H.266/VVC. In the proposed loop filter, the densely connected units are specially designed to extract feature maps, and fully decompose videos. By densely connection between layers, the designed units can reuse feature maps, and re- duce the redundancy of features.