Comparative Objective Analysis of video quality Between H.265/HEVC and H.264/AVC

BackgroundCone-beam computed tomography (CBCT) is a three-dimensional (3D) imaging method designed for routine target verification of cancer patients during radiotherapy. The images are reconstructed from a sequence of projection images obtained by the on-board imager attached to a radiotherapy machine. CBCT images are usually stored in a health information system, but the projection images are mostly abandoned due to their massive volume. To store them economically, in this study, a deep learning (DL)-based super-resolution (SR) method for compressing the projection images was investigated.MethodsIn image compression, low-resolution (LR) images were down-sampled by a factor from the high-resolution (HR) projection images and then encoded to the video file. In image restoration, LR images were decoded from the video file and then up-sampled to HR projection images via the DL network. Three SR DL networks, convolutional neural network (CNN), residual network (ResNet), and generative adversarial network (GAN), were tested along with three video coding-decoding (CODEC) algorithms: Advanced Video Coding (AVC), High Efficiency Video Coding (HEVC), and AOMedia Video 1 (AV1). Based on the two databases of the natural and projection images, the performance of the SR networks and video codecs was evaluated with the compression ratio (CR), peak signal-to-noise ratio (PSNR), video quality metric (VQM), and structural similarity index measure (SSIM).ResultsThe codec AV1 achieved the highest CR among the three codecs. The CRs of AV1 were 13.91, 42.08, 144.32, and 289.80 for the down-sampling factor (DSF) 0 (non-SR) 2, 4, and 6, respectively. The SR network, ResNet, achieved the best restoration accuracy among the three SR networks. Its PSNRs were 69.08, 41.60, 37.08, and 32.44 dB for the four DSFs, respectively; its VQMs were 0.06%, 3.65%, 6.95%, and 13.03% for the four DSFs, respectively; and its SSIMs were 0.9984, 0.9878, 0.9798, and 0.9518 for the four DSFs, respectively. As the DSF increased, the CR increased proportionally with the modest degradation of the restored images.ConclusionsThe application of the SR model can further improve the CR based on the current result achieved by the video encoders. This compression method is not only effective for the two-dimensional (2D) projection images, but also applicable to the 3D images used in radiotherapy.

While current video quality assessment research predominantly revolves around resolutions of 4 K and beyond, targeted at ultra high-definition (UHD) displays, effective video quality for mobile video streaming remains primarily within the range of 480 p to 1080 p. In this study, we conducted a comparative analysis of the quality of experience (QoE) for widely implemented video codecs on mobile devices, specifically Advanced Video Coding (AVC), its successor High-Efficiency Video Coding (HEVC), and Google’s VP9. Our choice of 720 p video sequences from a newly developed database, all with identical bitrates, aimed to maintain a manageable subjective assessment duration, capped at 35–40 min. To mimic real-time network conditions, we generated stimuli by streaming original video clips over a controlled emulated setup, subjecting them to eight different packet-loss scenarios. We evaluated the quality and structural similarity of the distorted video clips using objective metrics, including the Video Quality Metric (VQM), Peak Signal-to-Noise Ratio (PSNR), Video Multi-Method Assessment Fusion (VMAF), and Multi-Scale Structural Similarity Index (MS-SSIM). Subsequently, we collected subjective ratings through a custom mobile application developed for Android devices. Our findings revealed that VMAF accurately represented the degradation in video quality compared to other metrics. Moreover, in most cases, HEVC exhibited an advantage over both AVC and VP9 under low packet-loss scenarios. However, it is noteworthy that in our test cases, AVC outperformed HEVC and VP9 in scenarios with high packet loss, based on both subjective and objective assessments. Our observations further indicate that user preferences for the presented content contributed to video quality ratings, emphasizing the importance of additional factors that influence the perceived video quality of end users.

The compression capability of several generations of video coding standards is compared by means of peak signal-to-noise ratio (PSNR) and subjective testing results. A unified approach is applied to the analysis of designs, including H.262/MPEG-2 Video, H.263, MPEG-4 Visual, H.264/MPEG-4 Advanced Video Coding (AVC), and High Efficiency Video Coding (HEVC). The results of subjective tests for WVGA and HD sequences indicate that HEVC encoders can achieve equivalent subjective reproduction quality as encoders that conform to H.264/MPEG-4 AVC when using approximately 50% less bit rate on average. The HEVC design is shown to be especially effective for low bit rates, high-resolution video content, and low-delay communication applications. The measured subjective improvement somewhat exceeds the improvement measured by the PSNR metric.

High efficiency video coding (HEVC) standard, introduced by the joint collaborative team on video coding (JCT-VC), is the newest international standard for video compression. This standard provides more compression and better video quality, compared with the previous standards such as H.264/AVC. The higher compression efficiency in HEVC is achieved at the cost of increasing the computational load. Intra prediction unit is among the high computational stages in the HEVC encoder. There are proposed 35 intra prediction modes in HEVC, to improve the compression efficiency. To reduce the computational load of intra prediction, HEVC uses a preprocessing step, called Rough Mode Decision (RMD). A number of best prediction modes are selected by the RMD stage and then, using Rate Distortion Optimization (RDO) process, the encoder selects the best prediction mode among them. In this paper a two stage algorithm is proposed for fast intra mode decision in HEVC encoder, in the first stage of our proposed algorithm the number of the tested modes in RMD is reduced from 35 to 19. In the second stage, the number of the tested modes in the RDO process is reduced as well. In order to evaluate the performance of the proposed method two encoding profiles, main and main10 were used in the experiments. Experimental results indicate that the proposed method achieves 14.4 and 7.2 % reduction in the encoding time compared with the HEVC reference software in all intra-main and all intra-main10 configurations, respectively. Meanwhile, it imposes minimum reduction in the coding efficiency among the compared methods.

Video is the main driver of bandwidth use, accounting for over 80 per cent of consumer Internet traffic. Video compression is a critical component of many of the available multimedia applications, it is necessary for storage or transmission of digital video over today's band-limited networks. The majority of this video is coded using international standards developed in collaboration with ITU-T Study Group and MPEG. The MPEG family of video coding standards begun on the early 1990s with MPEG-1, developed for video and audio storage on CD-ROMs, with support for progressive video. MPEG-2 was standardized in 1995 for applications of video on DVD, standard and high definition television, with support for interlaced and progressive video. MPEG-4 part 2, also known as MPEG-2 video, was standardized in 1999 for applications of low- bit rate multimedia on mobile platforms and the Internet, with the support of object-based or content based coding by modeling the scene as background and foreground. Since MPEG-1, the main video coding standards were based on the so-called macroblocks. However, research groups continued the work beyond the traditional video coding architectures and found that macroblocks could limit the performance of the compression when using high-resolution video. Therefore, in 2013 the high efficiency video coding (HEVC) also known and H.265, was released, with a structure similar to H.264/AVC but using coding units with more flexible partitions than the traditional macroblocks. HEVC has greater flexibility in prediction modes and transform block sizes, also it has a more sophisticated interpolation and de blocking filters. In 2006 the VC-1 was released. VC-1 is a video codec implemented by Microsoft and the Microsoft Windows Media Video (VMW) 9 and standardized by the Society of Motion Picture and Television Engineers (SMPTE). In 2017 the Joint Video Experts Team (JVET) released a call for proposals for a new video coding standard initially called Beyond the HEVC, Future Video Coding (FVC) or known as Versatile Video Coding (VVC). VVC is being built on top of HEVC for application on Standard Dynamic Range (SDR), High Dynamic Range (HDR) and 360° Video. The VVC is planned to be finalized by 2020. This book presents the new VVC, and updates on the HEVC. The book discusses the advances in lossless coding and covers the topic of screen content coding. Technical topics discussed include: • Beyond the High Efficiency Video Coding • High Efficiency Video Coding encoder • Screen content • Lossless and visually lossless coding algorithms • Fast coding algorithms • Visual quality assessment • Other screen content coding algorithms Overview of JPEG Series

Video is the main driver of bandwidth use, accounting for over 80 per cent of consumer Internet traffic. Video compression is a critical component of many of the available multimedia applications, it is necessary for storage or transmission of digital video over today's band-limited networks. The majority of this video is coded using international standards developed in collaboration with ITU-T Study Group and MPEG. The MPEG family of video coding standards begun on the early 1990s with MPEG-1, developed for video and audio storage on CD-ROMs, with support for progressive video. MPEG-2 was standardized in 1995 for applications of video on DVD, standard and high definition television, with support for interlaced and progressive video. MPEG-4 part 2, also known as MPEG-2 video, was standardized in 1999 for applications of low- bit rate multimedia on mobile platforms and the Internet, with the support of object-based or content based coding by modeling the scene as background and foreground. Since MPEG-1, the main video coding standards were based on the so-called macroblocks. However, research groups continued the work beyond the traditional video coding architectures and found that macroblocks could limit the performance of the compression when using high-resolution video. Therefore, in 2013 the high efficiency video coding (HEVC) also known and H.265, was released, with a structure similar to H.264/AVC but using coding units with more flexible partitions than the traditional macroblocks. HEVC has greater flexibility in prediction modes and transform block sizes, also it has a more sophisticated interpolation and de blocking filters. In 2006 the VC-1 was released. VC-1 is a video codec implemented by Microsoft and the Microsoft Windows Media Video (VMW) 9 and standardized by the Society of Motion Picture and Television Engineers (SMPTE). In 2017 the Joint Video Experts Team (JVET) released a call for proposals for a new video coding standard initially called Beyond the HEVC, Future Video Coding (FVC) or known as Versatile Video Coding (VVC). VVC is being built on top of HEVC for application on Standard Dynamic Range (SDR), High Dynamic Range (HDR) and 360° Video. The VVC is planned to be finalized by 2020. This book presents the new VVC, and updates on the HEVC. The book discusses the advances in lossless coding and covers the topic of screen content coding. Technical topics discussed include: • Beyond the High Efficiency Video Coding • High Efficiency Video Coding encoder • Screen content • Lossless and visually lossless coding algorithms • Fast coding algorithms • Visual quality assessment • Other screen content coding algorithms Overview of JPEG Series

Video is the main driver of bandwidth use, accounting for over 80 per cent of consumer Internet traffic. Video compression is a critical component of many of the available multimedia applications, it is necessary for storage or transmission of digital video over today's band-limited networks. The majority of this video is coded using international standards developed in collaboration with ITU-T Study Group and MPEG. The MPEG family of video coding standards begun on the early 1990s with MPEG-1, developed for video and audio storage on CD-ROMs, with support for progressive video. MPEG-2 was standardized in 1995 for applications of video on DVD, standard and high definition television, with support for interlaced and progressive video. MPEG-4 part 2, also known as MPEG-2 video, was standardized in 1999 for applications of low- bit rate multimedia on mobile platforms and the Internet, with the support of object-based or content based coding by modeling the scene as background and foreground. Since MPEG-1, the main video coding standards were based on the so-called macroblocks. However, research groups continued the work beyond the traditional video coding architectures and found that macroblocks could limit the performance of the compression when using high-resolution video. Therefore, in 2013 the high efficiency video coding (HEVC) also known and H.265, was released, with a structure similar to H.264/AVC but using coding units with more flexible partitions than the traditional macroblocks. HEVC has greater flexibility in prediction modes and transform block sizes, also it has a more sophisticated interpolation and de blocking filters. In 2006 the VC-1 was released. VC-1 is a video codec implemented by Microsoft and the Microsoft Windows Media Video (VMW) 9 and standardized by the Society of Motion Picture and Television Engineers (SMPTE). In 2017 the Joint Video Experts Team (JVET) released a call for proposals for a new video coding standard initially called Beyond the HEVC, Future Video Coding (FVC) or known as Versatile Video Coding (VVC). VVC is being built on top of HEVC for application on Standard Dynamic Range (SDR), High Dynamic Range (HDR) and 360° Video. The VVC is planned to be finalized by 2020. This book presents the new VVC, and updates on the HEVC. The book discusses the advances in lossless coding and covers the topic of screen content coding. Technical topics discussed include: • Beyond the High Efficiency Video Coding • High Efficiency Video Coding encoder • Screen content • Lossless and visually lossless coding algorithms • Fast coding algorithms • Visual quality assessment • Other screen content coding algorithms Overview of JPEG Series

ABSTRACTIn recent times, the applications of multimedia are rising in a greedy mode and hence the amount of video transactions is also increasing exponentially. This has shouted great demands on effective models on video encoding and also for reducing the transmission channel congestion. This research work introduces a managing technique termed weighted encoding for High-Efficiency Video Coding (HEVC). HEVC, also termed as MPEG-H Part 2 and H.265 is a video compression standard that is widely utilized AVC (H.264 or MPEG-4 Part 10). When compared to AVC, HEVC grants double the ratio of data compression at a similar level of quality of the video or considerably enhanced video quality at a similar bit rate. This work intends to optimize the weight that adopted in HEVC for encoding. For this, this paper proposes a new Iterative based propagation update in the water wave Optimization Algorithm (IPU-WWO), which is the improved form of Water wave Optimization (WWO). The performance of proposed IPU-WWO is compared over other conventional methods like Artificial Bee Colony (ABC), Firefly (FF), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with respect to Peak Signal to Noise Ratio (PSNR). By doing the encoding process, it minimizes the video size with perceptually better quality video or PSNR.

This paper presents the results of empirical testing of high-efficiency video coding (HEVC) for standard-definition (SD), high-definition (HD), and ultra-high-definition (UHD), including data from hundreds of encoding tests across a variety of bitrates. The paper then compares HEVC quality results with the quality achieved by common Motion Picture Experts Group 2 (MPEG-2) and advanced video coding (AVC) distribution profiles to determine HEVC bitrate recommendations for SD, HD, and UHD distribution. The paper presents the following recommended bitrates for HEVC encoding: SD at 860 kbits/sec, HD at 2703 kbits/sec, and UHD at 23,922 kbits/sec. These represent a 45% bitrate savings, compared to AVC. HEVC encoding was done using x265 version 1.2, and all quality measurements were done using the peak signal-to-noise ratio and structural similarity index. The paper uses the HEVC Main (8-bit) profile to allow quality matching with H.264 and MPEG-2 8-bit encoding.

The High Efficiency Video Coding (HEVC) standard (ITU-T H.265 and ISO/IEC 23008-2) has been developed with the main goal of providing significantly improved video compression compared with its predecessors. In order to evaluate this goal, verification tests were conducted by the Joint Collaborative Team on Video Coding of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29. This paper presents the subjective and objective results of a verification test in which the performance of the new standard is compared with its highly successful predecessor, the Advanced Video Coding (AVC) video compression standard (ITU-T H.264 and ISO/IEC 14496-10). The test used video sequences with resolutions ranging from 480p up to ultra-high definition, encoded at various quality levels using the HEVC Main profile and the AVC High profile. In order to provide a clear evaluation, this paper also discusses various aspects for the analysis of the test results. The tests showed that bit rate savings of 59% on average can be achieved by HEVC for the same perceived video quality, which is higher than a bit rate saving of 44% demonstrated with the PSNR objective quality metric. However, it has been shown that the bit rates required to achieve good quality of compressed content, as well as the bit rate savings relative to AVC, are highly dependent on the characteristics of the tested content.

Fuzzy logic control has been employed to improve the rate control mechanism for a MPEG2 video encoder. The data rate of compressed video is controlled by video encoders for either variable bit rate (VBR) or constant bit rate (CBR) applications. In VBR video transmission, it is considered to be more efficient to regulate the video rate by the video coder than by network management in order to avoid network congestion and maintain stable video quality. This rationale can also be applied to CBR transmission. Two fuzzy-logic-based rate control techniques are proposed which maintain the buffer occupancy within a specified range. In the proposed technique for VBR applications, a video quality measure is taken as the crucial control parameter. In CBR rate control, the video data rate or the buffer occupancy is also considered as a fuzzy logic variable. The proposed techniques are designed to control either data rate or video quality, depending on the mode of transmission, i.e. CBR or VBR for the MPEG2 encoder. The performance is compared to a typical VBR MPEG video coder with fixed quantiser step sizes for VBR and also to the CBR video coder with MPEG2 TM5 at typical channel rates. Simulation results are presented with peak signal-to-noise ratio, data rate variation and buffer occupancy as the performance measures.

Advanced Video Coding (AVC) also known as H.264 is a popular video coding standard which is used in many image processing applications. In order to fulfill the increasing demand for better video quality with less complex designs, High Efficiency Video Coding (HEVC) also known as H.265 came into picture. HEVC provides better quality with half the bit rate required for processing in comparison with AVC. In case of AVC, the design of deblocking filter is more complex due to the filtering decisions, whereas HEVC is less complex and also supports parallel processing. As there is a need for high throughput and less complex deblocking filter, a novel dual standard deblocking filter is proposed in this work which supports both AVC and HEVC. In particular, a novel filtering order which enables four edges to be processed simultaneously has been proposed for both AVC and HEVC. For AVC, the proposed design takes 18 clock cycles to process a 16 × 16 macroblock and for HEVC standard, the proposed design takes 12 clock cycles to process a 16 × 16 coding transform unit (CTU). In addition to this, the proposed design supports parallel processing concept in HEVC. The proposed design is synthesized using Xilinx ISE 14.7 and is mapped to xc5vlx30-1ff324 Virtex-5 Field programmable gate array (FPGA). Thus it helps in increasing the execution speed of the design and it is also noted that there is a reduction in number of clock cycles needed for processing when compared with the existing architectures. The physical implementation of the proposed architecture is also carried out using Semiconductor Lab (SCL) 180 nm process node.

Video is the main driver of bandwidth use, accounting for over 80 per cent of consumer Internet traffic. Video compression is a critical component of many of the available multimedia applications, it is necessary for storage or transmission of digital video over today's band-limited networks. The majority of this video is coded using international standards developed in collaboration with ITU-T Study Group and MPEG. The MPEG family of video coding standards begun on the early 1990s with MPEG-1, developed for video and audio storage on CD-ROMs, with support for progressive video. MPEG-2 was standardized in 1995 for applications of video on DVD, standard and high definition television, with support for interlaced and progressive video. MPEG-4 part 2, also known as MPEG-2 video, was standardized in 1999 for applications of low- bit rate multimedia on mobile platforms and the Internet, with the support of object-based or content based coding by modeling the scene as background and foreground. Since MPEG-1, the main video coding standards were based on the so-called macroblocks. However, research groups continued the work beyond the traditional video coding architectures and found that macroblocks could limit the performance of the compression when using high-resolution video. Therefore, in 2013 the high efficiency video coding (HEVC) also known and H.265, was released, with a structure similar to H.264/AVC but using coding units with more flexible partitions than the traditional macroblocks. HEVC has greater flexibility in prediction modes and transform block sizes, also it has a more sophisticated interpolation and de blocking filters. In 2006 the VC-1 was released. VC-1 is a video codec implemented by Microsoft and the Microsoft Windows Media Video (VMW) 9 and standardized by the Society of Motion Picture and Television Engineers (SMPTE). In 2017 the Joint Video Experts Team (JVET) released a call for proposals for a new video coding standard initially called Beyond the HEVC, Future Video Coding (FVC) or known as Versatile Video Coding (VVC). VVC is being built on top of HEVC for application on Standard Dynamic Range (SDR), High Dynamic Range (HDR) and 360° Video. The VVC is planned to be finalized by 2020. This book presents the new VVC, and updates on the HEVC. The book discusses the advances in lossless coding and covers the topic of screen content coding. Technical topics discussed include: Beyond the High Efficiency Video CodingHigh Efficiency Video Coding encoderScreen contentLossless and visually lossless coding algorithmsFast coding algorithmsVisual quality assessmentOther screen content coding algorithmsOverview of JPEG Series

Steganography is the art of hiding information in something else. It is favorable over encryption because encryption only hides the meaning of the information; whereas steganography hides the existence of the information. The existence of a hidden image decreases Peak Signal to Noise Ratio (PSNR) and increases Mean Square Error (MSE) values of the stego image. We propose an approach to improve PSNR and MSE values in stego images. In this method a transformation is applied to the secret image, concealed within another image, before embedding into the cover image. The effect of the transformation is tested with Least Significant Bit (LSB) insertion and Discrete Cosine Transformation (DCT) techniques. MSE and PSNR are calculated for both techniques with and without transformation. Results show a better MSE and PSNR values when a transformation is applied for LSB technique but no significant difference was shown in DCT technique.

In recent years, the applications of multimedia are rising in greedy mode and hence the amount of video transactions are also increasing exponentially. It has been shouted a great demands for researchers to develop an effective models for video encoding & decoding, compression, bandwidth allocation, transmission channel and congestion control. This research work mainly focusses on to develop an efficient video compression standard algorithm named as High Efficiency Video Coding (HEVC). It proposes a new improved Iterative based propagation update in water wave Optimization Algorithm (IPU-WWO) that optimize the weights in adopted HEVC encoding. The proposed HEVC produces double the ratio of data compression at the similar level of quality of the video and considerably enhanced video quality with same bit rate compared with Advanced Video Coding (AVC). The performance of proposed IPU-WWO is compared with other conventional methods like Artificial Bee Colony (ABC), Firefly (FF), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with respect to Peak Signal to Noise Ratio (PSNR). The experimental results show that proposed method produces better compression rate of 3.03%, 4.98%, 6.82%, and 8.38% compared with PSO, GA, FF, and ABC, respectively with high PSNR for the block size of 4. Moreover, the proposed method outperforms the state-of-the-art fast encoding algorithms for in terms of compression and performance.