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Purpose: To evaluate if the dose equivalent to 2 Gy per fraction (EQD2)(α/β=3Gy) at 0.1cm³, 1cm³, and 2cm³ of vagina in vaginal-cuff-brachytherapy (VBT) (high-dose-rate [HDR] ¹⁹²Ir-source) ± external-beam-irradiation (EBRT) is associated with toxicity in post-operative endometrial carcinoma (P-EC). Material and methods: From June 2014 till November...
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... Such a quantization parameter allocation method lacks adaptability and will inevitably lead to a waste of bitrate and a reduction in rate-distortion performance. Since quantization will bring distortion to the compressed video data; it is particularly important to develop efficient and low-complexity quantizers and study efficient quantization algorithms [22,23]. During the HEVC standardization process, many improvements to the quantization method were proposed. ...
High-Efficiency Video Coding (HEVC) is one of the most widely studied coding standards. It still uses the block-based hybrid coding framework of Advanced Video Coding (AVC), and compared to AVC, it can double the compression ratio while maintaining the same quality of reconstructed video. The quantization module is an important module in video coding. In the process of quantization, quantization parameter is an important factor in determining the bitrate in video coding, especially in the case of limited channel bandwidth. It is particularly important to select a reasonable quantization parameter to make the bitrate as close as possible to the target bitrate. Aiming at the problem of unreasonable selection of quantization parameters in codecs, this paper proposes using a differential evolution algorithm to assign quantization parameter values to the coding tree unit (CTU) in each frame of 360-degree panoramic video based on HEVC so as to strike a balance between bitrate and distortion. Firstly, the number of CTU rows in a 360-degree panoramic video frame is considered as the dimension of the optimization problem. Then, a trial vector is obtained by randomly selecting the vectors in the population for mutation and crossover. In the mutation step, the algorithm generates a new parameter vector by adding the weighted difference between two population vectors to a third vector. And the elements in the new parameter vector are selected according to the crossover rate. Finally, the trial vector is regarded as the quantization parameter of each CTU in the CTU row to encode, and the vector with the least rate distortion is selected. The algorithm will produce the optimal quantization parameter combination for the current video. The experimental results show that compared to the benchmark algorithm of HEVC reference software HM-16.20, the proposed algorithm can provide a bit saving of 1.86%, while the peak signal-to-noise ratio (PSNR) can be improved by 0.07 dB.
... In [40], the luminance masking effect was considered, and the size of the quantization step was determined for each transform unit on the basis of intensity. Perceptual quantization has been adopted in several studies [10], [11] to convert the floatingpoint format into the integer format. An adaptive perceptual quantizer (PQ) was developed in [10], in which the codeword was designed by considering the minimum detectable contrast and luminance distribution of the HDR content. ...
High-dynamic range (HDR) images, which provide realistic visual perception, have attracted considerable attention in various applications. A simple method for obtaining HDR images is to fuse multiple low-dynamic range (LDR) images captured under varying exposures. We proposed a method of efficiently encoding multi-exposure images using multi-view High Efficiency Video Coding (MV-HEVC) along with intensity mapping function (IMF) to create high-quality HDR images. The inter-view prediction in MV-HEVC eliminates redundancy between multi-exposure images. To achieve high-efficiency MV-HEVC-based multi-exposure image coding, two modifications to the rate-distortion optimization (RDO) process performed during encoding were proposed. First, the distortion in the rendered HDR image is used to replace the original distortion, which depends on the LDR image. Second, to balance distortion and rate in RDO, a new Lagrange multiplier is derived from the derivative of the new distortion with respect to the rate. A model of the distortion in the HDR domain was constructed and expressed as a function of the distortion in the LDR domain. The proposed scheme utilizes the conventional 8-bit codec to compress multi-exposure LDR images and generate an HDR image in the decoder. The experimental results indicate that the proposed technique outperforms HDR image coding schemes in which HEVC Range Extension and JPEG-XT coding standards are used.
... The latest VVC test model (VTM) includes several coding tools specifically designed for HDR content; these include luma mapping with chroma scaling and luma-adaptive deblocking [6]. In the wider research community, many other approaches have also been proposed to enhance HDR video compression performance, typically focusing on perceptual quantisation [7,8], rate-distortion optimisation [9] and solving chroma leakage problems [10]. ...
It is well known that high dynamic range (HDR) video can provide more immersive visual experiences compared to conventional standard dynamic range content. However, HDR content is typically more challenging to encode due to the increased detail associated with the wider dynamic range. In this paper, we improve HDR compression performance using the effective bit depth adaptation approach (EBDA). This method reduces the effective bit depth of the original video content before encoding and reconstructs the full bit depth using a CNN-based up-sampling method at the decoder. In this work, we modify the MFRNet network architecture to enable multiple frame processing, and the new network, multi-frame MFRNet, has been integrated into the EBDA framework using two Versatile Video Coding (VVC) host codecs: VTM 16.2 and the Fraunhofer Versatile Video Encoder (VVenC 1.4.0). The proposed approach was evaluated under the JVET HDR Common Test Conditions using the Random Access configuration. The results show coding gains over both the original VVC VTM 16.2 and VVenC 1.4.0 (w/o EBDA) on JVET HDR tested sequences, with average bitrate savings of 2.9% (over VTM) and 4.8% (against VVenC) based on the Bjontegaard Delta measurement. The source code of multi-frame MFRNet has been released at https://github.com/fan-aaron-zhang/MF-MFRNet.
... However, the state-of-the-art preprocessing-based video coding schemes can not generate the optimal preprocessed frames for HDR video contents because they are modeled for SDR signal characteristics. Most of the HDR video coding researches are only focus on developing a new PQ for effectively quantizing real HDR videos to 10-bit HDR videos preserving their perceptual quality [13], [29], [30]. Thus, there are no preprocessing researches to improve the compression efficiency of HEVC main10 profile for quantized 10-bit HDR videos. ...
... Most of the preprocessing-based HDR video coding researches are only focus on developing a new PQ for effectively quantizing real HDR videos to 10-bit HDR videos preserving their perceptual quality [13], [29], [30]. Thus, since there are no preprocessing researches to improve the com- In the case of 'Kayak' video at QP=27, surprisingly, the decoded video of our PVC scheme was evaluated as better perceptual quality than that of the original HM16.17. ...
The final consumer of videos is mostly human. Therefore, if videos can be compressed by fully utilizing the perception characteristics of human visual systems (HVS), the bitrates of the compressed videos can be significantly reduced with subjective visual quality degradation as little as possible. Based on this, we newly propose a learning-based Just Noticeable Distortion (JND)-directed preprocessing scheme for perceptual video compression, especially for 10-bit High Dynamic Range (HDR) videos, which is called the HDR-JNDNet. Our HDR-JNDNet effectively suppresses the perceptual redundancy of 10-bit HDR video signals so that the compression efficiency can be significantly enhanced for the HEVC main10 profile encoder. To our best knowledge, our work is the first approach to training a CNN-based model to directly generate the JND-directed suppressed frames of 10-bit HDR video with the negligible perceptual quality difference between the decoded frames for the original HDR video input with and without the preprocessing by our HDR-JNDNet. Via intensive experiments, when the HDR-JNDNet is applied as preprocessing for the HDR video input before compression, it allows to remarkably save the required bitrates up to the maximum (average) 40.66% (18.37%) for 4K-UHD/HDR test videos, with little subjective video quality degradation without increasing the computational complexity.
... Due to storage, communication and computational constraints of the UASNs, encryption is not a preferred choice for security. Therefore, the authors have presented a trust model based on cloud theory for the UASNs [4]. The major drawback of this technique is that it has been implemented in a simulation environment and lack realtime implementation. ...
... High Dynamic Range can help emphasize the details in an image by capturing the subject with varying degrees of exposure and then combining the images to achieve a higher detail image. The author worked to utilize the Minimum Detectable Contrast for achieving the HDR image [4]. The presented technique preserves the information related to the contrast to help achieve better results. ...
High dynamic range (HDR) images have many practical applications because they offer an extended dynamic range and a more realistic visual experience. A HDR image is usually stored in floating-point format, so pre-processing is required to make the HDR image compatible with coding standards. A transfer function is also used to achieve better coding efficiency. Typically, HDR images are generated using several low dynamic range (LDR) images with different exposures. Instead of compressing the HDR image when it is generated from images with multiple exposures, this study proposes a technique to compress the multi-exposure images. The HDR image generation, as well as the multi-exposure images fusion, can be realized in the decoder. The proposed framework encodes the multi-exposure images using MV-HEVC where the inter-view redundancy is well exploited when an accurate intensity-mapping function between the multi-exposure images has been established. Multi-exposure image coding is used to produce a high-quality HDR image so the rate–distortion optimization (RDO) is modified by considering both the reconstruction quality of the current block and its effect on the multi-exposure fused image. A Lagrange multiplier is modified to maintain a balance between the rate and the modified distortion during the RDO process. Compared to encoding the generated HDR image using HEVC range extension, the experimental results show that the proposed technique achieves significant bitrate savings for equivalent quality in terms of HDR-VDP-2.
In the last decade, high dynamic range (HDR) image and video technology gained a lot of attention, especially within the multimedia community. Recent technological advancements made the acquisition, compression, and reproduction of HDR content easier, and that led to the commercialization of HDR displays and popularization of HDR content. In this context, measuring the quality of HDR content plays a fundamental role in improving the content distribution chain as well as individual parts of it, such as compression and display. However, HDR visual quality assessment presents new challenges with respect to the standard dynamic range (SDR) case. The first challenge is the new conditions introduced by the reproduction of HDR content, e.g. the increase in brightness and contrast. Even though accurate reproduction is not necessary for most of the practical cases, accurate estimation of the emitted luminance is necessary for the objective HDR quality assessment metrics. In order to understand the effects of display rendering on the quality perception, an accurate HDR frame reproduction algorithm was developed, and a subjective experiment was conducted to analyze the impact of different display renderings on subjective and objective HDR quality evaluation. Additionally, in order to understand the impact of color with the increased brightness of the HDR displays, the effects of different color spaces on the HDR video compression performance were also analyzed in another subjective study. Another challenge is to estimate the quality of HDR content objectively, using computers and algorithms. In order to address this challenge, the thesis proceeds with the performance evaluation of full-reference (FR) HDR image quality metrics. HDR images have a larger brightness range and higher contrast values. Since most of the image quality metrics are developed for SDR images, they need to be adapted in order to estimate the quality of HDR images. Different adaptation methods were used for SDR metrics, and they were compared with the existing image quality metrics developed exclusively for HDR images. Moreover, we propose a new method for the evaluation of metric discriminability based ona novel classification approach. Motivated by the need to fuse several different quality databases, in the third part of the thesis, we compare subjective quality scores acquired by using different subjective test methodologies. Subjective quality assessment is regarded as the most effective and reliable way of obtaining “ground-truth” quality scores for the selected stimuli, and the obtained mean opinion scores (MOS) are the values to which generally objective metrics are trained to match. In fact, strong discrepancies can easily be notified across databases when different multimedia quality databases are considered. In order to understand the relationship between the quality values acquired using different methodologies, the relationship between MOS values and pairwise comparisons (PC) scaling results were compared. For this purpose, a series of experiments were conducted using double stimulus impairment scale (DSIS) and pairwise comparisons subjective methodologies. We propose to include cross-content comparisons in the PC experiments in order to improve scaling performance and reduce cross-content variance as well as confidence intervals. The scaled PC scores can also be used for subjective multimedia quality assessment scenarios other than HDR.
Although high dynamic range (HDR) videos are a very fascinating way to represent real-world scenes, they need a huge amount of memory to store and transmit due to the high bit-depth. Thus, it is a major challenge for HDR video coding to efficiently compress them without sacrificing perceptual quality. Perceptual Quantizer (PQ) transfer function provides a solution to this problem, which is adopted as the HEVC Main 10 Profile-based Anchor. However, PQ is not adaptive to HDR contents, thus reducing the coding efficiency. In this paper, we propose adaptive transfer function based on PQ for HDR video compression, called adaptive PQ. Different from PQ which uses a fixed mapping curve from luminance to luma, the proposed transfer function adaptively maps luminance to luma according to HDR contents. Thus, adaptive PQ is able to efficiently utilize possible luma values. Moreover, adaptive PQ achieves better perceptual uniformity in the luminance range than PQ. Experimental results demonstrate that adaptive PQ achieves a significant performance improvement in HDR video coding over PQ in terms of visual quality and bitrate.
