Highly parallel HEVC decoding for heterogeneous systems with CPU and GPU

The High Efficiency Video Coding HEVC standard provides a higher compression efficiency than other video coding standards but at the cost of an increased computational load, which makes hard to achieve real-time encoding/decoding for ultra high-resolution and high-quality video sequences. Graphics Processing Units GPU are known to provide massive processing capability for highly parallel and regular computing kernels, but not all HEVC decoding procedures are suited for GPU execution. Furthermore, if HEVC decoding is accelerated by GPUs, energy efficiency is another concern for heterogeneous CPU+GPU decoding. In this paper, a highly parallel HEVC decoder for heterogeneous CPU+GPU system is proposed. It exploits available parallelism in HEVC decoding on the CPU, GPU, and between the CPU and GPU devices simultaneously. On top of that, different workload balancing schemes can be selected according to the devoted CPU and GPU computing resources. Furthermore, an energy optimized solution is proposed by tuning GPU clock rates. Results show that the proposed decoder achieves better performance than the state-of-the-art CPU decoder, and the best performance among the workload balancing schemes depends on the available CPU and GPU computing resources. In particular, with an NVIDIA Titan X Maxwell GPU and an Intel Xeon E5-2699v3 CPU, the proposed decoder delivers 167 frames per second (fps) for Ultra HD 4K videos, when four CPU cores are used. Compared to the state-of-the-art CPU decoder using four CPU cores, the proposed decoder gains a speedup factor of . When decoding performance is bounded by the CPU, a system wise energy reduction up to 36% is achieved by using fixed (and lower) GPU clocks, compared to the default dynamic clock settings on the GPU.EC/H2020/688759/EU/Low-Power Parallel Computing on GPUs 2/LPGPU

Decoding-complexity-aware HEVC encoding using a complexity–rate–distortion model

The energy consumption of Consumer Electronic (CE) devices during media playback is inexorably linked to the computational complexity of decoding compressed video. Reducing a CE device's the energy consumption is therefore becoming ever more challenging with the increasing video resolutions and the complexity of the video coding algorithms. To this end, this paper proposes a framework that alters the video bit stream to reduce the decoding complexity and simultaneously limits the impact on the coding efficiency. In this context, this paper (i) first performs an analysis to determine the trade-off between the decoding complexity, video quality and bit rate with respect to a reference decoder implementation on a General Purpose Processor (GPP) architecture. Thereafter, (ii) a novel generic decoding complexity-aware video coding algorithm is proposed to generate decoding complexity-rate-distortion optimized High Efficiency Video Coding (HEVC) bit streams. The experimental results reveal that the bit streams generated by the proposed algorithm achieve 29.43% and 13.22% decoding complexity reductions for a similar video quality with minimal coding efficiency impact compared to the state-of-the-art approaches when applied to the HM16.0 and openHEVC decoder implementations, respectively. In addition, analysis of the energy consumption behavior for the same scenarios reveal up to 20% energy consumption reductions while achieving a similar video quality to that of HM 16.0 encoded HEVC bit streams

Light Field Compression by Residual CNN Assisted JPEG

Light field (LF) imaging has gained significant attention due to its recent success in 3-dimensional (3D) displaying and rendering as well as augmented and virtual reality usage. Nonetheless, because of the two extra dimensions, LFs are much larger than conventional images. We develop a JPEG-assisted learning-based technique to reconstruct an LF from a JPEG bitstream with a bit per pixel ratio of 0.0047 on average. For compression, we keep the LF's center view and use JPEG compression with 50% quality. Our reconstruction pipeline consists of a small JPEG enhancement network (JPEG-Hance), a depth estimation network (Depth-Net), followed by view synthesizing by warping the enhanced center view. Our pipeline is significantly faster than using video compression on pseudo-sequences extracted from an LF, both in compression and decompression, while maintaining effective performance. We show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs

Image and Video Coding Techniques for Ultra-low Latency

The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.publishedVersionPeer reviewe

Learned Quality Enhancement via Multi-Frame Priors for HEVC Compliant Low-Delay Applications

Networked video applications, e.g., video conferencing, often suffer from poor visual quality due to unexpected network fluctuation and limited bandwidth. In this paper, we have developed a Quality Enhancement Network (QENet) to reduce the video compression artifacts, leveraging the spatial and temporal priors generated by respective multi-scale convolutions spatially and warped temporal predictions in a recurrent fashion temporally. We have integrated this QENet as a standard-alone post-processing subsystem to the High Efficiency Video Coding (HEVC) compliant decoder. Experimental results show that our QENet demonstrates the state-of-the-art performance against default in-loop filters in HEVC and other deep learning based methods with noticeable objective gains in Peak-Signal-to-Noise Ratio (PSNR) and subjective gains visually