Filter In High Efficiency Video Coding Research Articles

An Anti-Steganalysis HEVC Video Steganography With High Performance Based on CNN and PU Partition Modes

The steganography research of videos leads to excellent communication methods for transmitting secret message, and high efficiency video coding(HEVC) video is one popular steganographic carrier. This article proposes a prediction unit(PU) based wide residual-net steganography(PWRN) for HEVC videos. The visual quality distortion of modifying PUs is theoretically analyzed, which illustrates that modifying PUs only has a little negative effect on visual quality. Therefore, the data hiding method in this article allows to modify all types of PUs except for <inline-formula><tex-math notation="LaTeX">$2N\times 2N$</tex-math></inline-formula> to each other according to the secret data. In this way, high embedding efficiency is achieved, and the PU distributions in stego-videos can be kept similar to those of cover-videos, which is essential for resisting steganalysis. Meanwhile, a super-resolution convolutional neural network(CNN) with wide residual-net filter(WRNF) is proposed to replace the in-loop filter in HEVC for reconstructing I-pictures, which results in more precisely predicted P-pictures, and it further leads to less bitrate cost and better visual quality of stego-videos. The experimental results show that the proposed PWRN successfully resists the latest PU-targeted steganalysis algorithms, and compared with the state-of-the-art work, PWRN has achieved the lowest bitrate cost and the highest visual quality under the same capacity.

Optimized Sample Adaptive Offset Filter in HEVC

The high-efficiency video coding (HEVC) standard incorporates sample adaptive offset (SAO) as an in-loop filtering technique to reduce distortion. The fundamental tenet of SAO is to classify every reconstructed pixel and then simply add the offset to each group of pixels. The previous existing technique employs 32 bands in band offset (BO) calculation to determine the proper offset value to minimize the distortion in the SAO estimate algorithm for statistics collection and parametric determination procedure. The suggested method will reduce the necessary band count by 16 bands. It does not alter how edge offset is determined in any way. It offers enough and efficient room for creating superior band group predictions and selections, as well as an ideal decrease in the implemented area. Thus, reducing the number of bands to 16 also decreases the amount of storage needed to hold each pixel’s information in binary files by up to 0.65%.

Offset-Based In-Loop Filtering With a Deep Network in HEVC

With the great flexibility and performance of deep learning technology, there have been many attempts to replace existing functions inside video codecs such as High-Efficiency Video Coding (HEVC) with deep-learning-based solutions. One of the most researched approaches is adopting a deep network as an image restoration filter to recover distorted compressed frames. In this paper, instead, we introduce a novel idea for using a deep network, in which it chooses and transmits the side information according to the type of errors and contents, inspired by the sample adaptive offset filter in HEVC. A part of the network computes the optimal offset values while another part estimates the type of error and contents simultaneously. The combination of two subnetworks can address the estimation of highly nonlinear and complicated errors compared to conventional deep- learning-based schemes. Experimental results show that the proposed system yields an average bit-rate saving of 4.2% and 2.8% for the low-delay P and random access modes, respectively, compared to the conventional HEVC. Moreover, the performance improvement is up to 6.3% and 3.9% for higher-resolution sequences.

Quality Enhancement Network via Multi-Reconstruction Recursive Residual Learning for Video Coding

Lossy compression algorithms introduce multiple compression artifacts that severely decrease visual quality. These compression artifacts are highly related to texture contents, and the hierarchical coding units decision structure also brings multi-scale similarity to these artifacts. Current loop filters fail to utilize these characteristics to comprehensively remove compression artifacts. To this end, this letter proposes a novel quality enhancement method by adopting a multi-reconstruction recurrent residual network (MRRN). In particular, a modified recursive residual structure is designed to capture the multi-scale similarity of compression artifact. To effectively enhance frames with uneven noise, a multi-reconstruction structure is proposed, which outputs images with different denoise ratios and adaptively fuses them. Experimental results show that the proposed MRRN can improve coding efficiency up to 15.1% compared with the original loop filter in high-efficiency video coding. Averagely, 6.7%, 7.8%, 7.6% BD-rate reduction is achieved for all intra, low-delay P, and low-delay B, respectively. Meanwhile, as a quality enhancement method performed at encoder side, MRRN also achieves a good balance between coding performance and computational complexity compared to the state-of-the-art methods.

Residual Highway Convolutional Neural Networks for in-loop Filtering in HEVC.

High efficiency video coding (HEVC) standard achieves half bit-rate reduction while keeping the same quality compared with AVC. However, it still cannot satisfy the demand of higher quality in real applications, especially at low bit rates. To further improve the quality of reconstructed frame while reducing the bitrates, a residual highway convolutional neural network (RHCNN) is proposed in this paper for in-loop filtering in HEVC. The RHCNN is composed of several residual highway units and convolutional layers. In the highway units, there are some paths that could allow unimpeded information across several layers. Moreover, there also exists one identity skip connection (shortcut) from the beginning to the end, which is followed by one small convolutional layer. Without conflicting with deblocking filter (DF) and sample adaptive offset (SAO) filter in HEVC, RHCNN is employed as a high-dimension filter following DF and SAO to enhance the quality of reconstructed frames. To facilitate the real application, we apply the proposed method to I frame, P frame, and B frame, respectively. For obtaining better performance, the entire quantization parameter (QP) range is divided into several QP bands, where a dedicated RHCNN is trained for each QP band. Furthermore, we adopt a progressive training scheme for the RHCNN where the QP band with lower value is used for early training and their weights are used as initial weights for QP band of higher values in a progressive manner. Experimental results demonstrate that the proposed method is able to not only raise the PSNR of reconstructed frame but also prominently reduce the bit-rate compared with HEVC reference software.

Embedded multicore computing and applications

Embedded multicore computing and applications

HEVC SAO 인트라 모드 오프셋 값 조정을 통한 복잡도 감소

In this paper, we propose a complexity reduction method of sample adaptive offset (SAO), which is an in-loop filter in high-efficiency video coding (HEVC). In the conventional SAO, an offset value is calculated for each coding tree block (CTB) to minimize the error between the original and reconstructed images. In order to determine the optimal offset value, all offset candidates are examined and the offset value that leads to the smallest rate-distortion cost is chosen. Thus, SAO occupies a significant amount of the computational complexity in the HEVC encoder. In the proposed method, we determine the least-used band (LUB) by considering the statistical characteristics of offset values and without processing the offset value included in the LUB. Also, in the offset value decision stage, we check only a certain number of candidates rather than all of them. Experimental results show that the proposed method reduces the encoding time by approximately 8.15% without yielding a significant loss in terms of coding efficiency.