MulticoreWare

Media & Entertainment

Redefining Video Encoding with the x265 HEVC encoder

July 27, 2023

 

AuthorAshok Kumar Mishra is the Solution Architect for Media and Entertainment BU in MulticoreWare with 15+ years of experience in multimedia-embedded DSP systems and implementing and optimizing video codecs.

Santhoshini Sekar heads the Video Codecs and Video Solutions Engineering team at MulticoreWare, playing a key role in MulticoreWare’s x265 open-source HEVC encoder project and leads the x266 project.

Shivakumar Narayanan heads the Media and Entertainment BU focusing on AI-Enabled Video Codecs, Media Solutions, and Services.

Novel Motion-Compensated Spatio-Temporal Filtering Scheme for x265 Open-Source Video Encoder – Click here

Novel Histogram-Based Scene Change Detection Scheme for x265 Open-Source Video Encoder | Proceedings of the 2nd Mile-High Video Conference – Click here

Read the full article on OTT Verse

The x265 encoder, known for its compliance with the H.265/MPEG-HEVC video coding standard, has gained popularity among open-source frameworks, broadcast, and streaming service providers. It offers a range of tools and algorithmic optimizations to balance performance and output quality. It has been enhanced with two new techniques: motion-compensated spatio-temporal filtering (MCSTF) and histogram-based scene change detection (HCSD).

The MCSTF scheme effectively reduces noise in videos by utilizing motion vectors obtained from motion estimation across different video content resolutions. This allows for optimal temporal image correspondence for low-pass filtering, which reduces noise. The HCSD scheme automatically detects scene changes in videos, which can be used to improve coding efficiency.

The MCSTF scheme is beneficial in scenarios where the content contains a high noise level. It can be used by broadcasters and streaming service providers to reduce the bitrate of their over-the-top (OTT) content and allows for higher compression ratios without compromising visual quality. Video streaming platforms and content delivery networks (CDNs) can also use the MCSTF scheme to optimize bandwidth utilization.

The HCSD scheme is valuable for video indexing and scene analysis systems. It can be used by streaming platforms to optimize video segmentation, improve content categorization, and provide more accurate recommendations to viewers with reduced computational complexity.

Both the MCSTF and HCSD schemes are valuable tools for enhancing the x265 encoder. They can be used to reduce video transmission bit rate while maintaining visual quality, for both On-Premise and Cloud HEVC encoding use cases for the evolving needs of the Media and Entertainment industry.

What is next in the pipeline for x265 and the MulticoreWare Video Engineering team?

To enhance both video encoding efficiency and visual quality, the motion-compensated spatio-temporal filtering scheme and the histogram-based scene change detection scheme can be jointly optimized.

Investigate adaptive filtering approaches within the motion-compensated spatio-temporal filtering scheme by exploring methods that can dynamically modify the filtering parameters according to content attributes, such as scene complexity, motion intensity, or noise levels.

Delve into advanced methods to refine motion estimation within the motion-compensated spatio-temporal filtering scheme, aiming to enhance the accuracy and efficiency of motion vector estimation. This can involve exploring techniques such as hierarchical or predictive algorithms, which aim to improve temporal correspondence and achieve superior noise reduction.

Improve the accuracy of the histogram-based scene change detection scheme by investigating alternative statistical measures and feature representations. Additionally, consider the integration of machine learning techniques, such as deep neural networks, to learn intricate scene change patterns and enhance the overall detection performance.

Integrate multiple modalities, including audio and visual cues, to achieve a more robust and accurate scene change detection system. Explore the incorporation of audio analysis techniques, such as detecting sudden changes in audio energy or spectral content, into the histogram-based scheme. This integration will result in a multi-modal scene change detection solution that combines both visual and audio information for enhanced performance.

Examine energy-efficient optimizations for both the motion-compensated spatio-temporal filtering scheme and the histogram-based scene change detection scheme. Investigate techniques that can decrease computational complexity or power consumption without compromising coding gains and scene detection accuracy. Additionally, explore methods that have the potential to maintain or even improve the overall performance of the schemes in terms of coding gains and scene detection accuracy while operating in an energy-efficient manner.

Explore hardware acceleration techniques, such as utilizing GPUs or specialized hardware architectures, to boost the performance of both techniques. Investigate how parallel processing and optimized memory access patterns can accelerate the execution of these schemes, leading to real-time or near-real-time video encoding while minimizing power consumption. Additionally, examine how leveraging hardware acceleration can enhance the efficiency and effectiveness of the techniques, enabling faster processing and improved power efficiency.

By pursuing these technical ideas, the proposed techniques can be refined and extended, resulting in notable advancements in video encoding, noise reduction, scene change detection, and overall video processing.

Contact us : info@multicorewareinc.com

Share Via

Explore More

Oct 7 2024

Role of Embedded Systems in Overcoming Industry Challenges

The Media and Entertainment (M&E) industry is experiencing a period of rapid transformation, fueled by the growing demand for higher-quality content, faster delivery, and seamless interactivity.

Read more
Dec 18 2023 How-is-AI-Transforming-Video-Compression-and-Delivery

How is AI Transforming Video Compression and Delivery?

Delivering a substantial volume of content poses intricate challenges for broadcasters and streaming companies.

Read more
Oct 26 2023

The AI Revolution in Video Quality Enhancement

In the ever-evolving world of content streaming, the paramount factor is the level of quality. Whether you’re a streaming company, broadcaster, or an OTT/VOD service provider, enhancing the video quality of your content stands as a central objective.

Read more

GET IN TOUCH

    (Max 300 characters)