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We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately.


In modern video codecs, video frames are coded out-of-order following a hierarchical coding structure. The naive uniform video denoising, where denoising is applied indifferently to each frame, does not improve the compression performance. In our work, we only apply denoising to frames at layer 0 and 1. The denoising leads to a significant reduction of bit rates while maintaining temporal correlation. PSNR scores of the filtered frames decrease but PSNR scores of the unfiltered frames remain or even improve.


In order to achieve highly compact representation for videos, we propose an adaptive quantization based perceptual video coding (PVC) scheme in this paper. Because human only perceive the limited discrete-scale quality levels, the perceptual quantization is transformed into the problem of how to determine the maximum quantization parameter (Qp) under the same perceptual quality level. So, the relationship between perceptual quality level and quantization parameter is analyzed with the statistical way in this paper.


Adaptive HTTP streaming is the preferred method to deliver multimedia content in the internet. It provides multiple representations of the same content in different qualities (i.e. bit-rates and resolutions) and allows the client to request segments from the available representations in a dynamic, adaptive way depending on its context. The growing number of representations in adaptive HTTP streaming makes encoding of one video segment at different representations a challenging task in terms of encoding time-complexity.


Modern video codec uses arithmetic coding for entropy coding. The arithmetic coding asymptotically achieves the entropy bound provided the true probability distribution. Hence the compression efficiency heavily relies on the ability to capture the time-variant probability model in video signals. Variants of first-order linear probability model update schemes have been used in recent generation video codecs. Built on top of those, a multimodal estimation scheme that forms a higher order probability model update has been proposed in this work.


High-Efficiency Video Coding (HEVC) is the latest video coding standard which is developed by the Joint Collaborative Team on Video Coding (JCT-VC). To guarantee successful transmission and to make the best use of available network resources, an effective rate control mechanism plays a critical role in video coding standards. The coding performance can be maximised through the appropriate allocation of bits under the constraints of a total bit rate budget and the buffer size.