Current deep learning-based video encoding frameworks primarily focus on uniform global encoding, lacking specific optimizations for popular applications in recent years, such as video chat and conferencing. These applications typically feature key objectives that are more important than the background. Addressing this, this paper proposes a Depth Video Compression Algorithm based on Key Objectives, aimed at enhancing the encoding quality of these key elements while conservatively using the overall bitrate.

360 videos provide an omnidirectional view of the scene with extremely large data. Therefore, representing 360 videos with less data has become more and more important. Cube format is such a popular representation of 360 videos. However, we have to convert cube to Equirectangula(ERP) for displaying convenience. In this paper, we enhance Cube-to-ERP conversion performance by joint using Convolutional Neural Network(CNN) and classical interpolation method. The optimal threshold of boundary is derived according to geometry features of the cube-to-ERP format. This threshold is the guidance of how to combine CNN and classical interpolation method. Our experiment results prove that the derived threshold has a certain degree of guiding significance. Furthermore, we propose a new evaluation criterion with the help of Marsaglia model. It is much easier and more accurate to evaluate geometry conversion process.

In HEVC, bit-allocation model is based on the hierarchical control, which can divide video sequence into three levels: Group of Picture (GOP), frame and Coding Tree Unit (CTU). However, the fixed size of GOP fails to consider the influence of scene change in the video coding process, which may decrease the compression efficiency and reconstructed quality. In this paper, the main idea of the proposed algorithm is to detect the scene change efficiently, and then apply it in the rate control algorithm of HEVC to decrease the BD-rate and save the coding time.

In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.

Summary form only given. This paper mainly studies currently developing 3D video coding based on HEVC. HEVC-based 3D video coding mainly focuses on 3DTV and auto-stereoscopic video compression system. A variety of new encoding tools, such as inter-view motion prediction and depth modeling modes, have been added in 3D-HEVC. Although 3D-HEVC provides greater bit rate saving, it also brings the enormous encoding complexity increase. The coding time is increased correspondingly. It is necessary to reduce the encoding time. In this paper, a fast CU-sized partition algorithm is proposed for 3D-HEVC intra coding. The key point of this algorithm is to find the relationship between the texture characteristic and the sub-partition in each CU. It needs to determine whether the LCU can be subdivided to smaller CU according to the relationship. In order to reduce the redundancy of the human eye, just noticeable difference (JND) is a high efficiency model in the base of psychology and physiology. Instead of the time-consuming rate distortion optimization for coding mode decision, the variance of JND in each CU can be exploited to partition the coding unit according to human visual system characteristics. In other words, the larger blocks with higher JND variance will be subdivided to smaller blocks with lower JND variance. Consequently, the rules of CU preliminary partition are decided as follows: (a) For a 64×64 CU, if the variance of JND is larger than 0.25, the CU will be sub-divided into four 32×32 sub-blocks. (b) For a 32×32 CU, if the variance of JND is larger than 0.15, the CU will be sub-divided into four 16×16 sub-blocks. (c) For a 16×16 CU, if the variance of JND is larger than 0.10, the CU will be sub-divided into four 8×8 sub-blocks. The proposed algorithm is implemented based on HTM-13.1 reference software. The experiment condition is set up as "All Intra-Main" (AI-Main) configuration [1]. The quantization parameter (QP) values of texture are set to 25, 30, 35 and 40, respectively and the corresponding QPs of depth can be set to 34,39,42,45. The experimental results show that the fast intra mode decision algorithm provides over 29.25% encoding time saving on average with comparable rate distortion performance.

With the development of 3D video technology, many studies have paid attention to compression efficiency and rate distortion performance. When 3D videos are transmitted over error-prone channels, they may suffer significant quality degradation. In this paper, we combine multiview video coding (MVC) with multiple description coding (MDC) for robust transmission. The proposed scheme can give full consideration of both intra-view and inter-view correlation for better estimation. Furthermore, an adaptive mode decision is designed to generate a label as redundant information. The experiments show that the redundant information occupies just a few bits while the PSNR values of the reconstructed videos demonstrate a significant improvement.

High efficiency video coding (HEVC) is an emerging video compression standard, developed by the Joint Collaborative Team on Video Coding (JCT-VC). The aim of HEVC standardization effort is to save about 50% bit rate for equal perceptual video quality relative to H.264/AVC. Although HEVC provides greater bit rate saving, it also brings the enormous encoding complexity increase. In this paper, we propose a fast intra CU decision algorithm based on the texture characteristics of video. Furthermore, we also consider the coding bits of each CU as auxiliary information to refine the partition results. Experimental results show that the fast intra mode decision algorithm provides over 33% complexity reduction in terms of encoding time with negligible quality loss, compared with the original HEVC test model version HM-12.0+RExt-4.0rc2.

In this paper, a novel scheme of two-stage multiview image compression is proposed to create two-level reconstructed quality. Differently from the conventional multiview image compression algorithms, SIFT (Scale-Invariant Feature Transform) features matching from interview images are exploited to remove the correlations between multiple views. In the first stage coding, SIFT and RANSAC (RANdom SAmple Consensus) algorithms are combined to calculate the correlation matrix of interview, which then can be developed to obtain the coarse reconstruction of the current view. In the second stage coding, the reconstructed quality can be improved further by using the residual information. The experimental results have shown that at higher compression ratio, the proposed scheme can obtain better rate-distortion performance than intra coding in MVC (Multiview Video Coding). Furthermore, with the change of the compression ratio, the proposed scheme can achieve more stable reconstructed quality.

In this paper we present a SNR scalable extension design on three-dimensional video compression using High Efficiency Video Coding (3D-HEVC). A multi-loop decoder solution is integrated into the proposed scalable coding serves as the whole framework for the SNR scalable 3D-HEVC. To effectively improve the coding performance, an inter-layer texture prediction is extended into the proposed scalable scenario for texture views and depth maps. To further reduce complexity and bitrate, a novel inter-layer distortion less prediction method is added because of the smooth texture characteristic in depth maps.

This paper presents a novel compression algorithm to replace Depth Modeling Mode for coding the depth-map. The result demonstrates that the execution time is reduced on an average 54.7% while the BD-rate of virtual views increase only 1.47%.

Multiple description coding (MDC) is a promising technology for robust transmission over error-prone channels, which has attracted a lot research interests. The basic idea of MDC is to how to utilize redundant information of the descriptions for robust transmission. In view of practical applications, many MDC approaches have been proposed compatible with a certain standard codec, especially H.264/AVC. In this paper, we attempt to develop a novel MD video codec with generalized compatibility, which aims to the effective redundancy allocation from inter-/intra-descriptions. In [1], the redundancy allocation may be not enough effective due to frame level. As a result, in this paper, the redundant information will be taken into account at MB level.

Due to network congestion and delay sensibility, it is always a great challenge for video transmission over lossy network. Multiple description coding (MDC) is an attractive approach to solve this problem. It can efficiently combat packet loss without any retransmission thus satisfying the demand of real time services and relieving the network congestion. In view of perfect compatibility with the standard source and channel codec, temporal sampling based MDC has become a better choice for practical applications. However, for the frames switching from one scene to another temporal correlation may be destroyed by sampled in temporal domain, which may result in the false estimation when the related frames are lost at the side decoder. To address this problem, in this paper an improved MD coding based on temporal sampling is proposed to make sure the decoder can work correctly when scenes changing.

In this paper, we attempt to overcome the limitation of specific scalable video codec and apply FEC-MDC to a common video coder, such as the standard H.264. The proposed scheme is explained as follows. Firstly, according to motion vector changes, an original video sequence is divided into several sub-sequences as messages, so in each message better temporal correlation can be maintained for better estimation when information losses occur. Secondly, the standard H.264 encoder is used to encode the messages. Thirdly, based on priority encoding transmission, unequal protections are assigned in each message. Lastly, at the decoder, the segments whose priorities are not higher than the fraction of packets received can be recover totally.