What is CCTV Video Compression?
CCTV Video Compression refers to the technique or combination of techniques used to decrease the storage size of video files, captured through CCTV surveillance cameras, by removing redundant frames and employing other optimisation algorithms. With increasing demand for CCTV Surveillance Systems in business places as well as in urban infrastructural applications, CCTV Video Compression is becoming the need of the hour, to control storage costs while optimising for video quality. While storage compression technologies have been there in the Indian markets for more than a decade, these conventional compression technologies involved significant loss in video footage quality, thereby making it difficult to utilise the recordings later for identifying nuisance-makers, decipher vehicle registration numbers and such other applications.
The main problems posed by such conventional Video Compression technologies are:
- Video Resolution and Quality Issues, especially increasing demand for 8K and higher resolutions.
- Intensive CPU Utilisation due to complexity of the algorithm, thereby reducing CMOS battery life.
- Compatibility across Codecs, emerging standards and Video Formats.
- Inefficiency in inter-frame compression for scenes with complex textures and too many motions.
- Unintended noise and distortions may creep into the footage for conventional algorithms.
Types of CCTV Video Compression
Broadly there are two approaches to CCTV Video Compression:
Interframe Compression
In this CCTV cloud storage compression technique, the temporal redundancy between frames is leveraged by identifying the extent of similarity between successive frames, and then differences between the frames are encoded, rather than storing all the frames. It is also known as Frame Based Compression. This video compression technique starts with keyframes, also known as I-frames, then encoding the subsequent frames (called P-frames or Predictive Coded frames) based on their differences from the I-frame or an earlier P-frame. Then the algorithm predicts the B-frames (Bidirectional Prediction frames) based on the earlier and later I-frames and P-frames, employing motion compression for the movement of objects between the frames. JPEG, Wavelet and JPEG 2000 are the most commonly used formats with this type of compression.
Intraframe Compression
In this video compression technique, individual frames are processed independently as standalone images to reduce their storage size, and hence no frame is lost during the video compression process. It is also known as Stream Based Compression. The individual keyframes (also called I-frames) are compressed without setting any relation with their prior or subsequent frames, unlike Interframe Video Compression, and hence image quality is superior. This compression approach uses Discrete Cosine Transform (DCT compression), Vector Quantisation, Fractal Compression, and Discrete Wavelet Transform (DWT). This video compression technique is becoming increasingly popular in recent years in traffic management and advanced security applications. MPEG-2, MPEG-4, H.264 and MPEG-7 are the common formats preferred with this type of video compression.
How to Calculate CCTV Storage Requirements?
Cloud Storage for Surveillance Cameras is one of the most critical cost centres for any workplace facility, and hence organisations are always assessing ways to reduce their CCTV Cloud Storage costs. However, it is always recommended to get in touch with organisations having strong expertise in surveillance, like Transline Technologies, to calculate your CCTV Cloud Storage requirements, and make accurate projections of your video surveillance cloud storage costs.
Resolution
The resolution expectations vary according to the domain of application and even across organisations in the same domain of business. For instance, for smart traffic management, very high resolution footages need to be stored to identify defaulter vehicles by their registration plates.
Compression
Which compression algorithm your CCTV cloud storage system is using, determines the storage space requirements. Interframe Compression typically ensures stronger compression, and hence lower storage requirements.
Bitrate
Higher camera bitrate implies higher video quality and resultant requirement of greater video surveillance cloud storage capacity. Modern CCTV camera systems often use Variable Bit Rate (VBR), which adjusts the bitrate according to the extent of motion and activity in the scene being recorded.
Number of CCTV Cameras
The storage capacity requirement is directly proportional to the number of CCTV cameras whose video feeds are stored. Some modern CCTV storage compression algorithms optimise for motion detection, thereby saving CCTV cloud storage space when no movement is detected in the surveillance zone.
Storage Duration
The longer the duration for which CCTV video footage is to be stored, the higher the video surveillance cloud storage capacity required. Typical storage durations vary between 30 days, 45 days, 60 days and 90 days, depending on the business case or the organisation’s security compliance standards.
Key Challenges With CCTV Video Compression
Although CCTV Video Compression techniques are becoming increasingly popular, they have their own limitations. We would like to highlight some of these challenges, so that you are adapt your business expectations accordingly.
Quality Loss
Although modern video compression solutions smartly address quality loss issues, these cannot be absolutely eliminated – some choppiness and delays may creep into the video footage.
Incompatibility with Facial Recognition & Video Analytics Solutions
Due to some redundant frames being removed by the video compression algorithm or some frames compressed very sharply, the compressed footage may not remain compatible with AI powered Video Analytics or Facial Recognition algorithms. Though these solutions mostly work with live footages, which are processed before compression, some use cases may arise in the law-and-order enforcement space, where older footages may need to be processed with FR solutions for identification of criminals or nuisance-makers.
Processing Costs
While storage costs are controlled by using video compression software, processing costs may increase, due to the requirement of higher CPU utilisation capacities, more power consumption, and significant stress on CPU battery, thereby reducing its life.
Software Subscription Costs
Implementation of video compression software involves some costs, although at a net level, it helps cut down on storage costs. Hence, organisations prefer a SaaS model for CCTV video compression softwares, so that they can spread the costs across the lifetime, rather than incurring a one-time capital expenditure.
Reimagining CCTV Storage Efficiency with CAMSTORE
At Transline Technologies, we understand the pain-points of organisations, centred around high storage costs and ease of access of stored CCTV video footage. Hence, we have developed CAMSTORE, a revolutionary tool, which helps organisations save upto 90% on storage costs without compromising on video quality. We have solved most of the challenges discussed above with our advanced compression algorithms.
Unlike other CCTV Storage Compression Solutions, CAMSTORE ensures high video footage quality and lossless compression, while having the provisions for Cloud Integration and Scalability. Moreover, CAMSTORE is compatible with existing CCTV hardware infrastructure, and hence new CCTV cameras need not be purchased, thereby saving on capital expenditure. We have implemented CAMSTORE in Government organisations and private enterprises across Retail, Healthcare, Logistics & Warehousing, Manufacturing, E-Commerce, Banking & Finance and other sectors.
Connect with our Solutions Team today to understand how you can tackle the challenges associated with CCTV video compression and CCTV video storage for your organisation.
