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The process of installing it is somewhat straightforward:"}),"\n",(0,s.jsxs)(n.ol,{children:["\n",(0,s.jsx)(n.li,{children:"Download the source code for FFmpeg 7.0:"}),"\n"]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"git clone -b release/7.0 https://git.ffmpeg.org/ffmpeg.git ffmpeg\n"})}),"\n",(0,s.jsxs)(n.ol,{start:"2",children:["\n",(0,s.jsx)(n.li,{children:"Download the code for the XPSNR plug-in:"}),"\n"]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"git clone https://github.com/fraunhoferhhi/xpsnr\n"})}),"\n",(0,s.jsxs)(n.ol,{start:"3",children:["\n",(0,s.jsx)(n.li,{children:"Copy the plug-in files from XPSNR's code to FFmpeg's:"}),"\n"]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"cp xpsnr/libavfilter/* ffmpeg/libavfilter/\n"})}),"\n",(0,s.jsxs)(n.ol,{start:"4",children:["\n",(0,s.jsx)(n.li,{children:"Configure and compile FFmpeg:"}),"\n"]}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:"cd ffmpeg\n./configure ...\nmake -j $(nproc)\n"})}),"\n",(0,s.jsx)(n.h2,{id:"usage",children:"Usage"}),"\n",(0,s.jsx)(n.p,{children:"XPSNR is used as an FFmpeg filter, similarly to the built-in PSNR plugin:"}),"\n",(0,s.jsx)(n.pre,{children:(0,s.jsx)(n.code,{className:"language-bash",children:'./ffmpeg -i ref.mkv -i test.mkv -lavfi xpsnr="stats_file=xpsnr.log" -f null -\n'})}),"\n",(0,s.jsx)(n.h2,{id:"notes",children:"Notes"}),"\n",(0,s.jsxs)(n.ul,{children:["\n",(0,s.jsxs)(n.li,{children:["Being the reference implementation an FFmpeg plug-in and not a standalone binary is inconvenient for some workflows.\nCurrently, there are some attempts at making a ",(0,s.jsx)(n.a,{href:"https://github.com/gianni-rosato/sxpsnr",children:"standalone XPSNR binary"}),", although they are not very widespread yet."]}),"\n",(0,s.jsxs)(n.li,{children:["XPSNR uses a PSNR-like scoring system: a logarithmic scale in range 0..inf.\nThis is considerably harder to interpret than other metrics (e.g. ",(0,s.jsx)(n.a,{href:"/docs/metrics/SSIMULACRA2",children:"SSIMULACRA 2"})," or ",(0,s.jsx)(n.a,{href:"/docs/metrics/VMAF",children:"VMAF"}),");\nand the threshold to what constitutes good quality is content-dependent.\n(Preliminary testing seems to indicate that anything above a XPSNR score of 42.00 is visually lossless.)"]}),"\n",(0,s.jsxs)(n.li,{children:['XPSNR scores are computed independently for the luma (Y) and two chroma (U, V) components of videos,\nthus actually providing 3 scores.\nAlthough the developers argue that using the minimum of these 3 scores as "definitive score"\n(i.e. better correlates to human impressions),\nsome people in the community prefer using a weighted sum: ',(0,s.jsx)(n.code,{children:"(4 * XPSNR_Y + XPSNR_U + XPSNR_V) / 6"}),"."]}),"\n"]}),"\n",(0,s.jsx)(n.h2,{id:"comparing-to-ssimulacra-2",children:"Comparing to SSIMULACRA 2"}),"\n",(0,s.jsx)(n.p,{children:"The main advantage of XPSNR over SSIMULACRA 2 is that it is considerably faster to compute.\nXPSNR can achieve real-time speeds for 1080p 24 fps video, making it more convenient to quickly compare test encodes."}),"\n",(0,s.jsx)(n.p,{children:"Regarding their visual assessment capabilities, XPSNR and SSIMULACRA 2 seem to complement each other quite well,\nhaving both their own strong points and weak points with specific video content.\nIf possible, using both at the same time provides great benefits for video quality assessment and comparison."})]})}function h(e={}){const{wrapper:n}={...(0,r.R)(),...e.components};return n?(0,s.jsx)(n,{...e,children:(0,s.jsx)(d,{...e})}):d(e)}},8453:(e,n,i)=>{i.d(n,{R:()=>o,x:()=>a});var t=i(6540);const s={},r=t.createContext(s);function o(e){const n=t.useContext(r);return t.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function a(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(s):e.components||s:o(e.components),t.createElement(r.Provider,{value:n},e.children)}}}]);
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diff --git a/assets/js/runtime~main.dd547eb5.js b/assets/js/runtime~main.885f88f1.js
similarity index 79%
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rename to assets/js/runtime~main.885f88f1.js
index 3114838e..fb0c916b 100644
--- a/assets/js/runtime~main.dd547eb5.js
+++ b/assets/js/runtime~main.885f88f1.js
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-(()=>{"use strict";var e,a,f,b,d,c={},t={};function r(e){var a=t[e];if(void 0!==a)return a.exports;var f=t[e]={exports:{}};return c[e].call(f.exports,f,f.exports,r),f.exports}r.m=c,e=[],r.O=(a,f,b,d)=>{if(!f){var c=1/0;for(i=0;iThis section is in need of contributions. If you believe you can help, please see our Contribution Guide to get started as a contributor!
PSNR (Peak Signal-to-Noise Ratio) is one of the most widely used objective full-reference image and video quality metrics. It measures the ratio between the maximum possible signal power and the power of corrupting noise, expressed in decibels (dB). The metric is calculated using the Mean Squared Error (MSE) between a reference image and a distorted image. The theory and math behind PSNR are well covered in Wikipedia's PSNR entry.
+PSNR is widely used in video encoding applications because it is fast to compute, making it practical for real-time encoding decisions, and because it provides a consistent mathematical basis for comparing different encoding approaches. It is also used within video encoders to help make compression decisions.
+Within video encoders, PSNR plays a crucial role in rate-distortion optimization (RDO), which is the process of finding the optimal balance between bitrate and quality. Encoders use PSNR as an in-loop metric when evaluating different encoding decisions, such as mode selection, motion estimation, and quantization parameter (QP) selection.
+For example, when deciding between different prediction modes or block sizes, the encoder will calculate the PSNR impact of each option along with its bit cost to determine the best choice.
+While PSNR is widely used due to its simplicity and computational efficiency, it has several notable limitations. The metric has notoriously poor correlation with the human eye's perception of quality, as PSNR is highly sensitive to all pixel-level errors when many do not have any perceptually relevant impact. When PSNR is used to inform RDO in video encoding, it can lead to suboptimal quality decisions; to combat this, encoders have to creatively take the human visual system into account. We cover some of this in the Psychovisual entry.
+PSNR's weaknesses as a full reference distortion metric have led to the development of more advanced metrics like SSIM, VMAF, and XPSNR, which attempt to better model human visual perception.
+PSNR is commonly used for evaluating image and video compression algorithms, assessing streaming quality, comparing codec performance, and more. Many video encoding tools and analysis suites report PSNR values for different luma/chroma components (Y, U, V) separately, as well as a weighted average. Despite its limitations, PSNR continues to be an important tool in the video compression field, particularly when used in conjunction with other quality metrics and subjective evaluation methods.
This section is in need of contributions. If you believe you can help, please see our Contribution Guide to get started as a contributor!
The Structural Similarity Index Measure (SSIM) is a full-reference image and video quality metric that quantifies image fidelity degradation caused by processing such as lossy compression. Published in 2004 as part of an issue of IEEE Transactions on Image Processing, SSIM attempts to address the limitations of traditional metrics like Peak Signal-to-Noise Ratio (PSNR) by evaluating visual quality based on the structural information that humans naturally use to assess visual quality.
+SSIM works by comparing three key elements between the original and processed images: luminance, contrast, and structure. The luminance comparison measures the similarity of the average pixel intensities between the two images. These three comparisons are combined to produce a single similarity score ranging from -1 to 1, where 1 indicates perfect structural similarity.
+One of SSIM's main advantages is its ability to better align with human visual perception compared to traditional metrics. SSIM recognizes that pixels have strong inter-dependencies, especially when they are spatially close, which makes SSIM particularly effective at detecting changes in structural information that human observers would notice, such as blurring, blocking artifacts, or noise.
+As an in-loop metric in video encoders to improve decisionmaking, SSIM is more computationally expensive than PSNR, and doesn't always yield drastic improvements in fidelity per bit. Psychovisual encoder options are still necessary in many cases to achieve the best perceptual efficiency.
+In multimedia compression, SSIM can be more valuable in optimization scenarios where the goal is to maintain optimal perceptual quality for a given size. However, SSIM doesn't perfectly correlate with the human visual system; newer metrics like XPSNR and SSIMULACRA2 have been developed to correlate more closely with human perception. Modern variations and extensions of SSIM have been developed to address specific needs. Multi-scale SSIM (MS-SSIM) evaluates images at different scales to better match human visual perception. Color SSIM variants have been proposed to better handle color information, and SSIMULACRA (succeeded by SSIMULACRA2) was developed to improve correlation with human perception. These adaptations have improved upon SSIM's perceptual goals in an ever-changing multimedia compression landscape.