Video quality

About: Video quality is a research topic. Over the lifetime, 13143 publications have been published within this topic receiving 178307 citations.

Not All Packets Are Equal, Part 2: The Impact of Network Packet Loss on Video Quality

Abstract: For pt. 1 see ibid., vol. 13, no. 1, p.70-5 (2009). In this second part of a two-part article, the authors highlight the impact that different durations of IP packet loss have on the quality of experience for IP-based video streaming services. They describe the visual impairments that result from such packet losses and present the results of testing and analysis to compare impairments for different loss durations for both MPEG-2-encoded standard and high-definition services.

Adaptive source rate control for real-time wireless video transmission

Abstract: Hybrid ARQ schemes can yield much better throughput and reliability than static FEC schemes for the transmission of data over time-varying wireless channels. However these schemes result in extra delay. They adapt to the varying channel conditions by retransmitting erroneous packets, this causes variable effective data rates for current PCS networks because the channel bandwidth is constant. Hybrid ARQ schemes are currently being proposed as the error control schemes for real-time video transmission. An important issue is how to ensure low delay while taking advantage of the high throughput and reliability that these schemes provide for. In this paper we propose an adaptive source rate control (ASRC) scheme which can work together with the hybrid ARQ error control schemes to achieve efficient transmission of real-time video with low delay and high reliability. The ASRC scheme adjusts the source rate based on the channel conditions, the transport buffer occupancy and the delay constraints. It achieves good video quality by dynamically changing both the number of the forced update (intracoded) macroblocks and the quantization scale used in a frame. The number of the forced update macroblocks used in a frame is first adjusted according to the allocated source rate. This reduces the fluctuation of the quantization scale with the change in the channel conditions during encoding so that the uniformity of the video quality is improved. The simulation results show that the proposed ASRC scheme performs very well for both slow fading and fast fading channels.

Spatiotemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment

Abstract: The recently developed video multi-method assessment fusion (VMAF) framework integrates multiple quality-aware features to accurately predict the video quality. However, the VMAF does not yet exploit important principles of temporal perception that are relevant to the perceptual video distortion measurement. Here, we propose two improvements to the VMAF framework, called spatiotemporal VMAF and ensemble VMAF, which leverage perceptually-motivated space–time features that are efficiently calculated at multiple scales. We also conducted a large subjective video study, which we have found to be an excellent resource for training our feature-based approaches. In rigorous experiments, we found that the proposed algorithms demonstrate the state-of-the-art performance on multiple video applications. The compared algorithms will be made available as a part of the open source package in https://github.com/Netflix/vmaf .

Spatio-Temporal Deformable Convolution for Compressed Video Quality Enhancement.

Abstract: Recent years have witnessed remarkable success of deep learning methods in quality enhancement for compressed video. To better explore temporal information, existing methods usually estimate optical flow for temporal motion compensation. However, since compressed video could be seriously distorted by various compression artifacts, the estimated optical flow tends to be inaccurate and unreliable, thereby resulting in ineffective quality enhancement. In addition, optical flow estimation for consecutive frames is generally conducted in a pairwise manner, which is computational expensive and inefficient. In this paper, we propose a fast yet effective method for compressed video quality enhancement by incorporating a novel Spatio-Temporal Deformable Fusion (STDF) scheme to aggregate temporal information. Specifically, the proposed STDF takes a target frame along with its neighboring reference frames as input to jointly predict an offset field to deform the spatio-temporal sampling positions of convolution. As a result, complementary information from both target and reference frames can be fused within a single Spatio-Temporal Deformable Convolution (STDC) operation. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video quality enhancement in terms of both accuracy and efficiency.