Until recently, most of the data was structured and small in size, which meant it was easy to analyze it. Today, data is mostly unstructured or semi-structured and passes through many different media and devices. This makes it difficult to analyze and distribute, which calls for advanced methods.
Data science is primarily used for advanced analytics and making decisions and predictions. It is the study of where information comes from, what it represents and how it can be turned into a valuable resource in creating business and IT strategies.
Data science employs many tools, algorithms, and machine learning concepts to find patterns in raw data. It has myriad benefits.
Videos may be of different formats with different variables, such as containers and codecs. Therefore, they need to be prepared according to what and how their use would be.
Video encoding or transcoding is the process of compressing and converting digital video files from one standard digital video format into another. It makes video transmission over the Internet easy.
However, bit rate, the amount of data per second in the videos, dictates if users can easily watch them or if they take a long time to buffer.
Compression helps in reducing the bit rate and video size. It ensures that videos consume less space.
Data Science Aids Video Encoding
Videos can be compressed only when adequate information about them is available. To understand video formats, people need to understand the characteristics of videos and how they are used to define the format.
Videos are sequences of images called frames, displayed in order and defined based on previous frames. They can be compressed using only the information in them (intra frame) or using the information in others (inter frame).
Reducing video size:Data science helps reduce video size without damaging videos - by providing video format specifications, such as its length, resolution, codec and bit rate.
Speeding up encoding:Many things can make encoding slow--higher video resolutions; larger file sizes; output files set with high bit rate and frame rate; etc. Many things, such as computers of higher configuration and apt video encoding software, help speed it up too. Data science provides knowledge of all these factors, which makes manipulating them to increase encoding speed easy.
Optimizing quality:Once playback starts, the bit rate and the download server can be chosen by checking the algorithms that run in real-time or near real-time. Based on available data, video and live stream providers can offer a better viewing experience by using video content closer to users. They can also make optimal content caching decisions based on the users' viewing behavior. User experience can be further customized based on their feedback.
Thus, data science translates raw data into interpretable, actionable insights and helps choose metrics, models and tools that drive better performance.
Artificial Intelligence (AI) in Video Encoding
When using AI in video encoding, parameters can be set to a predefined set of values. AI helps the encoding software to analyze the quality of the encoded output, which means the encoding system finds and resolves any issues before transmission.
AI-enabled systems can learn from their actions and improve their performance. AI learns the patterns from data and optimizes the encoding configuration, thus enhancing encoding speed and reducing the output file size. This results in lesser bandwidth while streaming.
AI speeds up encoding by learning the complexity and characteristics of the input video, thus assigning the best compute resource for it. It improves workflows by efficiently handling the new codecs, video file formats, and delivery methods, using automation.
Advanced AI models can even predict optimal encoding settings and pre-processing tools for all videos. Overall, AI reduces the instances when humans have to fix picture quality.
At Uiza, we apply data science in all video streaming components, including video processing, video delivery and user analytics. Data science helps increase speed, enhance image quality and reduce file size for better streaming performance. We make video compression decisions using AI. Thus, data science and AI facilitate faster, high-quality video encoding and improve video, streaming, and service quality.
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