I have to compare image compression techniques like VQ, JPEG, WAVELET, and fractal. For this, the parameter to be compared is PSNR. Please tell me how I can calculate PSNR OF AN IMAGE which is COMPRESSED by different compression techniques. Plz explain with example. MATLAB: How to calculate MSE and PSNR in two different size image (block matching) Image Processing Toolbox index in position 2 exceeds array bounds (must not exceed 112). Normalized cross correlation pattern matching.

1. Matlab For Mac Student
2. Psnr Mse
3. Octave For Mac

Compare two encoder versions

Use general streams parameters, 11 video quality metrics and visually compare frames or separate blocks of the stream

Matlab For Mac Student

Optimize your encoder

Analyze the quality of encoded video against referenced RAW stream and identify the code portion causing the quality change

Key Features

Calculation of quality metrics: PSNR, APSNR, MSAD, MSE, SSIM, DELTA, VQM,NQI, VMAF and VMAF phone, VIF

Selection of ROI (region of interest) for metrics calculation

Display of essential statistics of encoded streams

Automatic selection of the similar first frame in two streams for analysis synchronization

Comparison of two encoded frames from different streams with a reference stream

Sharing comments between application instances and/or applications of Elecard StreamEye Studio set

Synchronization between applications of Elecard StreamEye Studio set (Binding mode)

Display of comparison results in graphs: metrics, quantizer, frame size, bit allocation etc.

Visual comparison of two encoded streams

Possibility to choose output YUV data format when saving decoded information

Bit allocation display

Automation through Command Line Interface

Saving data into CSV or text files

Software and Hardware specifications

Supported video formats

• MPEG 1/2 Video stream
• AVC/H.264 Video stream
• HEVC/H.265 Video stream
• VP9 Video stream
• AV1 Video stream
• VVC Video stream VTM 10.0 (preview version)

Supported media containers

• MPEG-2 Transport Stream
• MPEG-2 Program Stream
• MP4 file container
• MKV file container
• MXF file container
• AVI container
• IVF container
• FLV container

RAW formats

• I444
• IYUV
• NV12
• NV21
• P444
• RGB24
• RGB32
• UYVY (Y422, UYNV, HDYC)
• V210
• V400
• Y42B
• YUY2 (YUNV, V422, YUYV)
• YV12
• YV16
• YVYU
• .Y4M
• WIDE
• I400
• I422
• V444

System Requirements

• Windows® 7/8/10 (64-bit)
• Mac OS X 10.9 Mavericks and later
• Ubuntu 16.04, 18.04, 20.04 x64
• CentOS 7.6 x86_64
• Fedora 29

​

Screenshots

Follow this link to find the VVC video samples and estimate how Video Quality Estimator work with it.

Please fill in a short form to get free demo-version or product pricing.

Related products

StreamEye Studio

Elecard StreamEye Studio is a set of powerful software tools for video quality analysis designed for professional use in video compression, processing, communication and streaming media industries

Stream Analyzer

Elecard Stream Analyzer is a professional tool for syntax analysis of encoded media streams

New version 4.3 is released

Boro

IPTV Monitoring

Software solution for UDP, RTP, HTTP and HLS streams quality control and measurement of QoS and QoE parameters in all segments of distributed networks. Live stream monitoring.

Resources

• • Elecard Video Quality Estimator for Windows User Guide(PDF,346.28 KB)
  • Elecard Video Quality Estimator for Mac User Guide(PDF,1.44 MB)
  • Elecard Video Quality Estimator for Linux User Guide(PDF,1.29 MB)
• Command line tool manual

Video Tutorials

Learn more about Video Quality Estimator GUI and functionality

News & Events

Elecard releases new version of StreamEye Studio v. 4.6

Elecard has implemented support for the new VVC format in the video analysis products that are part of StreamEye Studio set.

Compute peak signal-to-noise ratio (PSNR) between images

Description

The PSNR block computes the peak signal-to-noise ratio, in decibels, between two images. This ratio is used as a quality measurement between the original and a compressed image. The higher the PSNR, the better the quality of the compressed, or reconstructed image.

The mean-square error (MSE) and the peak signal-to-noise ratio (PSNR) are used to compare image compression quality. The MSE represents the cumulative squared error between the compressed and the original image, whereas PSNR represents a measure of the peak error. The lower the value of MSE, the lower the error.

To compute the PSNR, the block first calculates the mean-squared error using the following equation:

In the previous equation, M and N are the number of rows and columns in the input images. Then the block computes the PSNR using the following equation:

In the previous equation, R is the maximum fluctuation in the input image data type. For example, if the input image has a double-precision floating-point data type, then R is 1. If it has an 8-bit unsigned integer data type, R is 255, etc.

Computing PSNR for Color Images

Different approaches exist for computing the PSNR of a color image. Because the human eye is most sensitive to luma information, you can compute the PSNR for color images by converting the image to a color space that separates the intensity (luma) channel, such as YCbCr. The Y (luma), in YCbCr represents a weighted average of R, G, and B. G is given the most weight, again because the human eye perceives it most easily. Compute the PSNR only on the luma channel.

Input

Input image, specified as scalar, vector, or matrix.

Psnr Mse

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Input image, specified as scalar, vector, or matrix.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | Boolean | fixed point

Output

Peak signal-to-noise ratio between images, returned as a scalar.

Dependencies

If the input is a fixed-point or integer data type, the block output is double-precision floating point. Otherwise, the block input and output are the same data type.

Data Types: double

Model Examples

Compare the quality of a noisy and denoised image from the PSNR value computed using the PSNR block.

Block Characteristics

Extended Capabilities

Octave For Mac

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Generates code only for double or single data types.

Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.