Analysis of the perceptual quality performance of different HEVC coding tools

Each new video encoding standard includes encoding techniques that aim to improve the performance and quality of the previous standards. During the development of these techniques, PSNR was used as the main distortion metric. However, the PSNR metric does not consider the subjectivity of the human visual system, so that the performance of some coding tools is questionable from the perceptual point of view. To further explore this point, we have developed a detailed study about the perceptual sensibility of different HEVC video coding tools. In order to perform this study, we used some popular objective quality assessment metrics to measure the perceptual response of every single coding tool. The conclusion of this work will help to determine the set of HEVC coding tools that provides, in general, the best perceptual response

A simplified subjective video quality assessment method based on signal detection theory

Proceedings of: Picture Coding Symposium (PCS 2012). Krakow, Poland, May 7-9, 2012.A simplified protocol and associated metrics based on Signal Detection Theory (SDT) for subjective Video Quality Assessment (VQA) is proposed with the aim of filling the gap existing between the lack of discrimination abilities of objective quality estimates, specially when perceptually motivated processing methods are involved and the costly normative subjective quality tests. The proposed protocol employs a reduced number of assessors and provides a quality ranking of the methods being evaluated. It is intended for providing the rapid experimental turn around necessary for developing algorithms. We have validated our proposal performing the test on a well-known result for the video coding community: namely, that the inclusion of an in-loop deblocking filter provides a quality enhancement. The results obtained corroborate this fact. A software interface to design and administrate the test is also made publicly available.This work has been partially supported by the regional project CCG10-UC3M/TIC-5304 (Comunidad Autónoma de Madrid - UC3M) and by National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.Publicad

The AV1 Constrained Directional Enhancement Filter (CDEF)

This paper presents the constrained directional enhancement filter designed for the AV1 royalty-free video codec. The in-loop filter is based on a non-linear low-pass filter and is designed for vectorization efficiency. It takes into account the direction of edges and patterns being filtered. The filter works by identifying the direction of each block and then adaptively filtering with a high degree of control over the filter strength along the direction and across it. The proposed enhancement filter is shown to improve the quality of the Alliance for Open Media (AOM) AV1 and Thor video codecs in particular in low complexity configurations.Comment: 5 page

Dynamically Reconfigurable Architectures and Systems for Time-varying Image Constraints (DRASTIC) for Image and Video Compression

In the current information booming era, image and video consumption is ubiquitous. The associated image and video coding operations require significant computing resources for both small-scale computing systems as well as over larger network systems. For different scenarios, power, bitrate and image quality can impose significant time-varying constraints. For example, mobile devices (e.g., phones, tablets, laptops, UAVs) come with significant constraints on energy and power. Similarly, computer networks provide time-varying bandwidth that can depend on signal strength (e.g., wireless networks) or network traffic conditions. Alternatively, the users can impose different constraints on image quality based on their interests. Traditional image and video coding systems have focused on rate-distortion optimization. More recently, distortion measures (e.g., PSNR) are being replaced by more sophisticated image quality metrics. However, these systems are based on fixed hardware configurations that provide limited options over power consumption. The use of dynamic partial reconfiguration with Field Programmable Gate Arrays (FPGAs) provides an opportunity to effectively control dynamic power consumption by jointly considering software-hardware configurations. This dissertation extends traditional rate-distortion optimization to rate-quality-power/energy optimization and demonstrates a wide variety of applications in both image and video compression. In each application, a family of Pareto-optimal configurations are developed that allow fine control in the rate-quality-power/energy optimization space. The term Dynamically Reconfiguration Architecture Systems for Time-varying Image Constraints (DRASTIC) is used to describe the derived systems. DRASTIC covers both software-only as well as software-hardware configurations to achieve fine optimization over a set of general modes that include: (i) maximum image quality, (ii) minimum dynamic power/energy, (iii) minimum bitrate, and (iv) typical mode over a set of opposing constraints to guarantee satisfactory performance. In joint software-hardware configurations, DRASTIC provides an effective approach for dynamic power optimization. For software configurations, DRASTIC provides an effective method for energy consumption optimization by controlling processing times. The dissertation provides several applications. First, stochastic methods are given for computing quantization tables that are optimal in the rate-quality space and demonstrated on standard JPEG compression. Second, a DRASTIC implementation of the DCT is used to demonstrate the effectiveness of the approach on motion JPEG. Third, a reconfigurable deblocking filter system is investigated for use in the current H.264/AVC systems. Fourth, the dissertation develops DRASTIC for all 35 intra-prediction modes as well as intra-encoding for the emerging High Efficiency Video Coding standard (HEVC)

Deep learning-based switchable network for in-loop filtering in high efficiency video coding

The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering