Bolt65 – performance-optimized HEVC HW/SW suite for Just-in-Time video processing

Piljić, Igor; Dragić, Leon; Duspara, Alen; Cobrnic, Mate; Mlinarić, Hrvoje; Kovač, Mario

doi:10.23919/mipro.2019.8756825

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Transform operations performed with TBs of different sizes are different and TBs are grouped before the transfer to the device. Gathering the same sized TBs can be done by resuming our previous work [20]. The prediction stage can be modified in a way that residual values are stored to memory location for particular block size.…”

Section: Proposed Gpu Parallelization Methodsmentioning

confidence: 99%

Performance engineering for HEVC transform and quantization kernel on GPUs

et al. 2020

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Continuous growth of video traffic and video services, especially in the field of high resolution and high-quality video content, places heavy demands on video coding and its implementations. High Efficiency Video Coding (HEVC) standard doubles the compression efficiency of its predecessor H.264/AVC at the cost of high computational complexity. To address those computing issues high-performance video processing takes advantage of heterogeneous multiprocessor platforms. In this paper, we present a highly performance-optimized HEVC transform and quantization kernel with all-zero-block (AZB) identification designed for execution on a Graphics Processor Unit (GPU). Performance optimization strategy involved all three aspects of parallel design, exposing as much of the application's intrinsic parallelism as possible, exploitation of high throughput memory and efficient instruction usage. It combines efficient mapping of transform blocks to thread-blocks and efficient vectorized access patterns to shared memory for all transform sizes supported in the standard. Two different GPUs of the same architecture were used to evaluate proposed implementation. Achieved processing times are 6.03 and 23.94 ms for DCI 4K and 8K Full Format, respectively. Speedup factors compared to CPU, cuBLAS and AVX2 implementations are up to 80, 19 and 4 times respectively. Proposed implementation outperforms previous work 1.22 times.

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Section: Proposed Gpu Parallelization Methodsmentioning

confidence: 99%

Performance engineering for HEVC transform and quantization kernel on GPUs

et al. 2020

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“…The result of the experiments showed an improvement in Peak Signal to Noise Ratio (PSNR), prompting the authors to claim that a higher "QoE" for endusers was achieved. The ShieldHEVC is a self-adapting mechanism for improving video quality in transmissions over VANETs [12]. This mechanism uses both video characteristics and network details in the process of finding the most appropriate amount of redundancy.…”

Section: Related Workmentioning

confidence: 99%

Secure and Efficient Video Transmission in VANET

Meddeb¹,

Meddeb-Makhlouf²,

Ayed³

2022

Int. j. commun. netw. inf. secur.

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Currently, vehicular communications have become a reality used by various applications, especially applications that broadcast video in real time. However, the video quality received is penalized by the poor characteristics of the transmission channel (availability, non-stationarity, the ration of signal-to-noise, etc.). To improve and ensure minimum video quality at reception, we propose in this work a mechanism entitled “Secure and Efficient Transmission of Videos in VANET (SETV)”. It's based on the "Quality of Experience (QoE)" and using hierarchical packet management. This last is based on the importance of the images of the stream video. To this end, the use of transmission error correction with uneven error protection has proven to be effective in delivering high quality videos with low network overhead. This is done based on the specific details of video encoding and actual network conditions such as signal to noise ratio, network density, vehicle position and current packet loss rate (PLR) not to mention the prediction of the future DPP.Machine learning models were developed on our work to estimate perceived audio-visual quality. The protocol previously gathers information about its neighbouring vehicles to perform distributed jump reinforcement learning. The simulation results obtained for several types of realistic vehicular scenarios show that our proposed mechanism offers significant improvements in terms of video quality on reception and end-to-end delay compared to conventional schemes. The results prove that the proposed mechanism has showed 11% to 18% improvement in video quality and 9% load gain compared to ShieldHEVC.

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Bolt65 – performance-optimized HEVC HW/SW suite for Just-in-Time video processing

Cited by 2 publications

References 9 publications

Performance engineering for HEVC transform and quantization kernel on GPUs

Performance engineering for HEVC transform and quantization kernel on GPUs

Secure and Efficient Video Transmission in VANET

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