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The new coding tools improved the performance for H.266/VVC but also brought challenges for hardware integer motion estimation (IME). First, the data dependency in deriving a predicted motion vector (PMV) is more severe. Second, the overhead of IME is increased by the complex partition mechanism. The challenges are tougher for IME in coding tree unit (CTU) level pipelined encoder. In this paper, we propose a hardware-friendly CTU-level IME algorithm with three innovative designs. First, a PMV prediction is proposed to derive PMVs in advance.

In many digital systems, the transmission bandwidth, as well as storage capacity, are usually very limited. This introduces challenges for both video transmission and video storage. One of the efficient solutions to this problem is to compress the down-sampled frames and then process up-sampling/super-resolution after decoding. To seek lower bit rates and further obtain high-quality up-sampled videos, this paper proposes a temporal down-sampling based
video coding system and a frame-recurrent enhancement based video upsampling strategy.

In compressed sensing (CS) based CMOS image sensors (CS-CIS), the ternary measurement matrix determines the compression performance in terms of decoded image quality versus sampling rate (data rate).In this paper, we propose a structured measurement matrix called Zigzag ordered Walsh matrix (ZoW). Firstly, the Walsh matrix is divided into several measurement patterns.

There is larger compression potential for surveillance video coding due to the inherent superredundancy in fixed camera scenarios, which is not fully utilized in the general-purpose MPEG-like video coders. In this paper, we propose a novel compression framework for indoor surveillance video in which the background and foreground humans are separately compressed. Since the indoor surveillance video’s background is static, the background is extracted and shared between frames.

In the paper highly ecient algorithm for lossless image coding is described. Its high data compaction performance is obtained at the expense of computational complexity, still being not as high as for other top rated methods. The algorithm is based on predictor blending principle, outputs of 27 dierent sub-predictors are combined, hence its name Blend-27. Eciency of Blend-27 is tested on a set of 15 usually used benchmark images, and compared to that of over 20 methods, including the best known ones.

Focal stack image sequences can be regarded as successive frames of videos, which are densely captured by focusing on a stack of focal planes. This type of data is able to provide focus cues for display technologies. Before the displays on the user side, focal stack video is possibly corrupted during compression, storage and transmission chains, generating error frames on the decoder side. The error regions are difficult to be recovered due to the focal changes among frames.

With the rapid development of 3D vision applications such as autonomous driving and the dramatic increase of point cloud data, it becomes critical to efficiently compress 3D point clouds. Recently, point-based point cloud compression has attracted great attention due to its superior performance at low bit rates. However, lacking an efficient way to represent the local geometric correlation well, most existing methods can hardly extract fine local features accurately. Thus it’s difficult for them to obtain high-quality reconstruction of local geometry of point clouds.