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In the development of video coding standard, decoder-side motion derivation technology has been proven to provide promising coding efficiency. With this type of technology, the motion information is derived at the decoder instead of being signaled in the bitstream by the encoder, and thus, the number of bits to be sent are reduced. A typical decoder-side derivation technology is template matching, which refines the motion by finding the closest match between neighboring reconstructed samples and corresponding reference samples in the reference pictures.

This paper proposes a novel quantization parameter (QP) derivation module that constructs several spatiotemporal characteristics into key-frame QP determination through an efficient pre-analysis progress. A series of simplified prediction modes and a histogram statistic are employed to model the reference quality that key-frames provide to subsequent frames. An adaptive delta-QP value is generated to address the conflict between the low compression efficiency of intra-only frames and the critical predictive basis of temporal-underlying frames.

Lightweight video compression has effectively alleviated the tension between growing transmission demands and expensive integration upgrades. Effective rate control algorithms are believed to be the crucial bottleneck for quality improvement during those ultra-high throughput coding processes. This paper proposes a novel rate control (RC) scheme that constructs a contextual adaptive bit estimation model through clustering historical compression information into block-gradient complexity categories.

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.