Interview Questions for Video Compression (MPEG, H.264, HEVC)

In H.264 video compression, both intra and inter prediction are techniques used to reduce redundancy and improve compression efficiency. They differ fundamentally in how they predict the pixel values of a macroblock (a block of pixels).

Intra prediction, also known as I-prediction or spatial prediction, predicts the pixels within a macroblock using only the already coded pixels within the same frame. Think of it like a sophisticated form of image interpolation; it analyzes the neighboring pixels and uses them to estimate the values of the pixels within the macroblock. This is independent of any other frames. This approach is computationally less intensive but results in larger file sizes because it doesn’t benefit from temporal redundancy.

Inter prediction, or P-prediction, leverages temporal redundancy—the similarity between consecutive frames in a video. It predicts the pixels within a macroblock by referencing previously coded macroblocks in different frames (usually the previous frame). The process involves motion estimation to find the best matching block in the reference frame and motion compensation to apply that match to the current macroblock. This leads to significantly higher compression ratios but also requires more complex computation.

Example: Imagine a video of a person slowly moving their head. In an I-frame (using only intra prediction), each frame is independently encoded. In a P-frame (using inter prediction), only the changes between frames are encoded, leveraging the similarity between frames. The difference—the motion—is a much smaller amount of data to store.

Motion estimation and compensation are crucial steps in inter prediction, allowing H.264 (and other codecs) to achieve high compression ratios.

Motion Estimation is the process of identifying the best matching block of pixels in a reference frame (usually the preceding frame) for a given block in the current frame. This is done by searching for the block that minimizes a difference metric, such as Mean Absolute Difference (MAD) or Sum of Absolute Differences (SAD). Algorithms like block matching algorithms (e.g., full search, three-step search) or more advanced techniques are used. The output of motion estimation is a motion vector, which indicates the displacement between the current block and its best match in the reference frame.

Motion Compensation utilizes the motion vector obtained from motion estimation to create a prediction of the current macroblock. The process involves shifting and copying the identified block from the reference frame to align it with the current macroblock’s position. The difference between the actual current macroblock and the compensated prediction (residual) is then encoded. Because the residual is typically much smaller than the original macroblock, this dramatically reduces the required data size.

Example: Consider a scene with a person walking. In successive frames, the person’s image will have moved. Motion estimation identifies this movement, and motion compensation uses that information to predict the person’s position in the current frame based on their position in the previous frame. Only the small differences (residuals, like the blurring around the moving person) need to be encoded.

HEVC (High Efficiency Video Coding) offers significant improvements over H.264, but also comes with trade-offs.

• Advantages of HEVC over H.264:
  • Higher Compression Efficiency: HEVC typically achieves about 50% better compression than H.264 at the same perceived quality, resulting in smaller file sizes for the same visual quality or higher quality for the same file size.
  • Improved Resolution Support: HEVC is designed to handle ultra-high definition (UHD) and even higher resolutions efficiently.
  • Better Rate-Distortion Performance: HEVC generally exhibits a better rate-distortion curve, offering a more favorable trade-off between bitrate (file size) and distortion (visual quality).
• Disadvantages of HEVC over H.264:
  • Higher Computational Complexity: Encoding and decoding HEVC video is considerably more computationally intensive than H.264, requiring more powerful processors and more processing time.
  • Increased Memory Requirements: HEVC demands more memory resources compared to H.264, both for encoding and decoding.
  • Limited Compatibility: Although HEVC adoption is growing, it’s not as universally supported as H.264 in various devices and platforms.

Practical Application: The choice between HEVC and H.264 depends on the specific application. HEVC is ideal for applications where high compression efficiency is crucial, such as streaming UHD video or storing large video archives. However, H.264 remains a preferred choice where computational resources are limited or wider compatibility is essential.

Rate-distortion optimization (RDO) is a fundamental principle in video compression that aims to find the best balance between the bitrate (rate) used to encode a video and the resulting distortion (distortion) in the reconstructed video. It seeks to minimize distortion for a given bitrate or minimize the bitrate for a given distortion level.

The concept is straightforward: lower bitrates lead to smaller file sizes, but also higher distortion (lower quality). Higher bitrates give better quality but larger file sizes. RDO algorithms analyze various encoding choices at each stage of the compression process, measuring both the rate and distortion associated with each choice. They select the option that provides the best trade-off between these two competing factors. This is often represented as a rate-distortion curve, with rate on one axis and distortion on the other.

Practical Application: RDO is used in every stage of video encoding, from mode decision (choosing between intra and inter prediction) to quantization parameter selection. A well-designed RDO algorithm ensures that the encoded video achieves the optimal balance between compression efficiency and visual quality for a given target bitrate or quality level. In professional settings, encoders allow the user to control the desired bitrate or quality level which are then optimized using RDO.

Quantization is a crucial step in video compression that significantly affects both the quality and size of the compressed video. It’s a lossy process, meaning information is discarded, leading to some data loss and reduced quality. However, the benefit is the considerable reduction in data size.

During quantization, the transform coefficients (resulting from Discrete Cosine Transform or DCT) are divided by a quantization step size (controlled by the Quantization Parameter or QP). A larger quantization step size leads to larger quantization intervals, causing many coefficients to be rounded to zero or smaller values. This results in fewer non-zero coefficients and thus smaller data size. Conversely, a smaller quantization step size results in finer quantization, preserving more of the original information, leading to higher quality but larger file size.

Example: Imagine a grayscale image with pixel values ranging from 0 to 255. If we use a quantization step size of 16, values between 0-15 would be quantized to 0, 16-31 would be quantized to 16, and so on. This reduces the number of unique values, achieving compression, but also introduces loss of detail (quantization error).

In practical settings: The QP is a key parameter in video encoding that controls the balance between quality and size. Higher QPs result in smaller file sizes and lower quality, while lower QPs result in larger files and higher quality.

Transform coding is a core component of video compression, used to decorrelate pixel data and concentrate energy into fewer coefficients, improving compression efficiency. Several transform coding techniques have been employed in video compression standards.

• Discrete Cosine Transform (DCT): This is the most widely used transform in video compression, including MPEG, H.264, and HEVC. DCT converts spatial domain data (pixel values) into the frequency domain. It efficiently represents smooth regions of an image with a few low-frequency coefficients and represents high-frequency components (details and edges) with higher-frequency coefficients. The high-frequency coefficients are typically smaller in magnitude, making them easier to discard during quantization.
• Discrete Sine Transform (DST): DST is similar to DCT but is better suited for certain types of signals. Some video compression standards use DST for specific parts of the encoding process.
• Wavelet Transform: Wavelet transforms offer multiresolution analysis, providing a more adaptive representation of image data. This can lead to better compression performance in some scenarios, particularly for images with sharp edges and fine details. They are not as widespread in video coding as DCT, but they’re explored in some advanced compression techniques.

Practical Application: The choice of transform significantly impacts the compression efficiency and the overall quality of the compressed video. DCT’s efficiency and computational simplicity have made it a dominant choice across generations of video compression standards. However, newer algorithms and future video codecs might explore alternative transforms to achieve even better compression.

Entropy coding is the final step in video compression, aiming to further compress the quantized transform coefficients by representing them using fewer bits. It leverages the statistical properties of the data to assign shorter codes to more frequent symbols (coefficients) and longer codes to less frequent symbols. This reduces the overall size of the encoded data without introducing any further loss of information.

Several entropy coding techniques exist:

• Huffman coding: A variable-length coding scheme that assigns shorter codes to more frequent symbols. It’s relatively simple but not always optimal.
• Context-Adaptive Binary Arithmetic Coding (CABAC): Used in H.264 and HEVC, CABAC is a more sophisticated technique that adapts to the context (surrounding symbols) during encoding, allowing for even better compression.
• Context-Adaptive Variable-Length Coding (CAVLC): A simpler variable-length coding technique used in H.264 as an alternative to CABAC, offering a trade-off between complexity and compression efficiency.

Practical Application: Entropy coding is essential for achieving high compression ratios in video compression. It’s the last stage before the compressed video bitstream is packaged and ready for transmission or storage. In professional video encoding workflows, the choice of entropy coding method impacts the compression efficiency and computational requirements.

In H.264, a macroblock is a fundamental processing unit of 16×16 pixels. Think of it as a small tile within a larger image. The encoder processes each macroblock independently, applying various compression techniques. This division simplifies the encoding process, allowing for parallel processing and efficient management of video data. Each macroblock is analyzed for motion and texture, and then coded using discrete cosine transform (DCT) to remove redundancy. This transformed data is then quantized and entropy coded to reduce the bitrate.

For example, imagine a video of a moving car. The encoder divides the video frame into numerous 16×16 macroblocks. Some blocks might contain mostly uniform sky, requiring minimal data to represent. Others, with the car’s details, will need more bits. This allows for efficient allocation of resources – higher detail areas get more bits, simpler areas get fewer, minimizing the overall file size.

HEVC, or H.265, uses Coding Tree Units (CTUs) as its fundamental processing unit. Unlike the fixed-size macroblocks of H.264, CTUs are square blocks of variable size, typically ranging from 8×8 to 64×64 pixels, depending on the chosen configuration. This flexibility allows for better adaptation to the video content. A CTU is further recursively partitioned into smaller Coding Units (CUs), Prediction Units (PUs), and Transform Units (TUs). This quadtree-based partitioning scheme allows for more efficient coding of complex scenes.

Imagine a video with a lot of fine detail in one area and large uniform blocks in another. HEVC can use larger CTUs for the uniform area, requiring less processing and fewer bits. In the detail area, it can use smaller CTUs to accurately represent the complex information, ensuring high fidelity. This adaptive partitioning is a key improvement over H.264’s fixed-size macroblocks.

Lambda (λ) is a crucial parameter in rate-distortion optimization (RDO), a core element of video compression. RDO aims to find the optimal balance between the bitrate (rate) and the visual distortion (distortion) introduced by compression. Lambda acts as a weighting factor that determines the relative importance of these two competing factors. A higher lambda prioritizes a lower bitrate (smaller file size) even at the cost of higher distortion (poorer visual quality). A lower lambda prioritizes higher quality, even if it means a larger file size.

The encoder uses lambda to select the best coding mode (e.g., intra vs. inter prediction, transform size) for each coding unit. It does this by evaluating the rate-distortion cost for different options and choosing the one that minimizes the weighted sum of rate and distortion. This allows the encoder to dynamically adjust its compression strategy based on the content’s complexity and the desired quality level.

The deblocking filter is a post-processing step in video compression that smooths out the artificial blocky artifacts that often appear at the boundaries of macroblocks (in H.264) or coding units (in HEVC). These artifacts are a byproduct of the transform and quantization steps during compression. The filter analyzes the pixel values across block boundaries and subtly adjusts them to reduce the visibility of these discontinuities. It’s a crucial step in improving the subjective quality of the decoded video.

Imagine a compressed image with visible square blocks. The deblocking filter works to make these boundaries less noticeable, creating a smoother, more natural-looking image. This process avoids a loss of detail and preserves the overall sharpness while removing the objectionable blocking effects.

Sample Adaptive Offset (SAO) is a post-processing filter in HEVC designed to further refine the visual quality of the decoded video. Unlike the deblocking filter, which primarily addresses blockiness, SAO tackles other visual artifacts by locally adapting the pixel values. It analyzes the residual signal (the difference between the original and reconstructed signal) and applies different offset values to different groups of samples. This adaptive approach helps to correct for small inconsistencies or inaccuracies that remain after other processing steps.

For example, SAO can effectively reduce banding artifacts, often seen as horizontal or vertical stripes in smoothly shaded areas of an image. It can also mitigate other distortions caused by the quantization process. By adjusting the pixel values based on local characteristics, SAO significantly improves the subjective quality without significantly increasing the bitrate.

CAVLC (Context-Adaptive Variable Length Coding) and CABAC (Context-Adaptive Binary Arithmetic Coding) are both entropy coding techniques used in video compression to further reduce the size of the compressed bitstream. Both are context-adaptive, meaning they adjust their coding strategy based on the statistical properties of the data being encoded. However, they differ significantly in their approach.

CAVLC uses variable-length codes to represent the quantized transform coefficients. It is simpler to implement than CABAC but generally achieves a slightly lower compression efficiency. CABAC, on the other hand, utilizes binary arithmetic coding, which offers better compression ratios but is more complex and computationally expensive. In modern codecs, CABAC is often preferred for its superior efficiency despite the higher computational cost, especially at lower bitrates.

Hierarchical B-frames leverage the concept of bidirectional prediction to further improve compression efficiency. In video compression, B-frames are predicted from both past and future frames (I-frames and P-frames), unlike P-frames which are only predicted from past frames. Hierarchical B-frames extend this concept by introducing multiple levels of prediction. A hierarchical structure is created by utilizing several B-frames, predicting them from multiple P-frames and/or I-frames at different levels in the hierarchy, allowing for more flexible and powerful prediction.

This arrangement allows for more accurate motion compensation and reduced redundancy in complex scenes with significant motion. The trade-off is increased decoding complexity as the decoder must handle the complex dependency relationships between the hierarchical B-frames. This approach is beneficial for achieving higher compression ratios in scenarios demanding higher efficiency, particularly in high-motion videos, while maintaining visual quality.

H.264 and HEVC (also known as H.265) use a system of profiles and levels to define the capabilities of an encoder and decoder. Think of it like car models: a profile specifies the features (e.g., 4K resolution, high frame rate support), while a level defines the performance limits (e.g., maximum resolution, bitrate, and computational complexity).

H.264 Profiles: Examples include Baseline, Main, High, and Extended profiles. The Baseline profile is the most basic, suitable for low-complexity devices. The High profile adds features like 8×8 transform and better motion estimation, leading to higher compression efficiency. Extended profiles incorporate even more advanced features.

H.264 Levels: Levels are defined by parameters like maximum picture size, maximum bitrate, and maximum frame rate. A higher level means the encoder/decoder can handle more demanding video content. For instance, Level 5.1 supports high-resolution video suitable for Blu-ray discs.

HEVC Profiles: HEVC also has various profiles like Main, Main 10, and Main Still Picture, each providing specific functionalities (Main 10 supports 10-bit color depth).

HEVC Levels: Similar to H.264, HEVC levels are designated based on resolution, bitrate, and frame rate capabilities. Higher levels allow for processing more complex video, such as 8K resolution videos.

In practice, choosing the right profile and level involves balancing compression efficiency, computational demands, and the capabilities of the target device. A mobile phone might use a low profile and level, while a high-end TV could support a more advanced profile and a high level.

Real-time video encoding and decoding present significant challenges due to the strict latency requirements. Imagine a video conference: delays are unacceptable. These challenges include:

• Computational complexity: Encoding and decoding high-resolution video at high frame rates demands considerable processing power. Real-time processing requires optimized algorithms and efficient hardware acceleration.
• Latency constraints: The entire process, from capturing the video to displaying it, must happen within a very short timeframe, typically under a few hundred milliseconds for interactive applications. Any increase in latency dramatically affects the user experience.
• Bitrate management: Controlling the bitrate in real-time is crucial for maintaining a balance between quality and bandwidth consumption. Dynamically adapting the bitrate based on network conditions is essential, especially in environments with fluctuating bandwidth.
• Error handling: Network issues can interrupt the video stream. Robust error handling and resilience mechanisms are needed to minimize the impact of packet loss or corruption on the video quality. Efficient error concealment techniques are vital.
• Power consumption: For mobile devices, efficient algorithms and hardware are critical for minimizing power consumption while maintaining real-time performance.

Addressing these challenges involves using optimized encoding algorithms (e.g., fast motion estimation), hardware acceleration (e.g., using GPUs), adaptive bitrate streaming (e.g., DASH), and robust error concealment methods. For example, techniques like forward error correction (FEC) add redundancy to the data to make it more resilient to errors.

Video interlacing is a technique where each frame is composed of two fields: one containing the odd lines and the other containing the even lines. This was primarily used in older analog television systems to reduce bandwidth requirements. There are two main types:

• Progressive scan: Each frame is displayed as a complete picture; all lines are scanned sequentially. This is the standard for modern displays and digital video.
• Interlaced scan: Frames are displayed by scanning odd lines first, then even lines, creating a complete image. This method was efficient for transmission but leads to noticeable flickering, especially with fast-moving objects, a phenomenon known as ‘interlace artifacts’.

De-interlacing is the process of converting interlaced video into progressive video. Several methods exist:

• Line doubling: Simply duplicating each line to create a progressive frame. This is the simplest method, but it can result in blurry images.
• Motion-adaptive de-interlacing: This technique analyzes the motion in the video and interpolates missing lines accordingly. It provides better quality than line doubling, especially for moving objects, but is more computationally intensive.
• Field adaptive de-interlacing: It utilizes motion estimation and line interpolation to improve video quality. Based on motion detection, it selects the best field or a blend of fields.
• Inverse telecine: This technique is specifically designed for de-interlacing film-based video, which often has a 3:2 pulldown interlacing pattern. It detects and removes the artifacts caused by this pattern, leading to higher quality.

The choice of de-interlacing method depends on factors like computational resources and desired quality. More sophisticated methods offer better quality but consume more processing power.

Video compression plays a vital role in reducing the bandwidth required for video streaming. Uncompressed video requires a massive amount of bandwidth. For example, a single second of uncompressed 1080p video can easily exceed 100 MB. This is impractical for streaming applications.

Compression algorithms like H.264 and HEVC drastically reduce the file size by removing redundant information and exploiting spatial and temporal redundancies in the video. They achieve this using techniques such as motion estimation, discrete cosine transform (DCT), and quantization. The lower the bitrate (bits per second), the smaller the file size and the lower the bandwidth required.

The impact of compression on bandwidth is directly proportional to the bitrate and resolution of the video. Higher resolutions and higher bitrates require more bandwidth. Adaptive bitrate streaming (ABR) technologies dynamically adjust the bitrate based on the available network bandwidth, ensuring smooth streaming even with fluctuating network conditions. This is why you often see video players adjusting quality based on network conditions.

Error resilience in video transmission focuses on minimizing the impact of errors introduced during transmission, such as packet loss or corruption. Several techniques are employed:

• Forward Error Correction (FEC): This method adds redundant data to the video stream. The receiver can use this redundancy to correct errors without requiring retransmission. FEC adds overhead but significantly improves robustness.
• Interleaving: This technique spreads out data across multiple packets. Even if a few packets are lost, the impact on the video is minimized because the data is not concentrated in a small area. Think of it like spreading your eggs across multiple baskets – if one basket breaks, you don’t lose all your eggs.
• Error Concealment: This involves using information from neighboring frames or pixels to reconstruct lost or corrupted data. This doesn’t recover the original data perfectly, but it produces a visually less disruptive effect than simply displaying a black screen.
• Robust encoding techniques: Advanced compression standards like HEVC incorporate features specifically designed to improve robustness to errors. These features are often related to better error propagation control within the compressed data.
• Retransmission: While not ideal for real-time applications due to latency concerns, retransmission schemes such as Automatic Repeat reQuest (ARQ) are used when acceptable delay is allowed.

The selection of error resilience techniques depends on the application’s requirements and the characteristics of the transmission channel. Real-time video streaming over unreliable networks might prioritize FEC and error concealment to maintain low latency, while less time-critical applications might rely more on retransmission.

Constant Bit Rate (CBR) encoding maintains a constant bitrate throughout the video. Think of it like a water faucet with a fixed flow rate. The amount of water (data) is the same over time.

Variable Bit Rate (VBR) encoding allows the bitrate to vary based on the complexity of the video content. For simple scenes with less detail, the bitrate is lower, and for complex scenes with rapid motion, the bitrate is higher. This is like a water faucet with a variable flow rate – more water when you need it, less when you don’t.

Advantages of CBR: CBR simplifies network management and buffering, as the bandwidth requirements are predictable. It’s suitable for applications where constant bandwidth is essential (e.g., broadcasting).

Disadvantages of CBR: CBR can be inefficient for videos with varying levels of detail. Simple scenes may have unnecessarily high bitrates, while complex scenes may suffer from quality degradation due to insufficient bitrate.

Advantages of VBR: VBR provides better compression efficiency by allocating more bits to complex scenes and fewer bits to simpler scenes, resulting in higher quality at the same average bitrate or lower bitrate for the same quality. This is generally more efficient for storage and streaming applications.

Disadvantages of VBR: VBR requires more sophisticated buffering and network management due to its fluctuating bitrate, adding complexity. It can lead to buffering issues if the network bandwidth cannot handle the peak bitrates.

In practice, the choice between CBR and VBR depends on the application’s needs and priorities. VBR is generally preferred for streaming and storage applications, while CBR may be suitable for broadcast or real-time applications with strict bandwidth constraints.

Video container formats, like AVI, MP4, MKV, and MOV, are essentially wrappers that store the compressed video and audio data along with metadata (information about the video, like resolution, codec, and frame rate). They don’t affect the compression itself, but they significantly impact features and compatibility.

MP4 (MPEG-4 Part 14): Widely compatible, supports various codecs (like H.264 and HEVC), good for streaming and portable devices. It’s a very common and versatile container.

MKV (Matroska): Highly flexible, supports a wide range of codecs and features (subtitles, multiple audio tracks), open-source, good for archiving, but compatibility can be an issue with some older players.

AVI (Audio Video Interleave): Older format, less efficient than modern containers, limited codec support, primarily used for older Windows-based systems.

MOV (QuickTime File Format): Developed by Apple, good compatibility with Apple devices, supports various codecs, but compatibility with non-Apple devices can be limited.

Advantages and Disadvantages Summary:

• Wide Compatibility: MP4 offers broad compatibility across devices and software.
• Feature Richness: MKV provides extensive support for features like multiple audio tracks and subtitles.
• Efficiency: Modern formats like MP4 are generally more efficient than older ones like AVI.
• Open Source vs. Proprietary: MKV is open-source; others may be proprietary.

Choosing a container format depends on factors like the target platform, desired features, and compatibility requirements. For web streaming, MP4 is generally preferred due to its wide support. For archiving and greater flexibility, MKV might be a better choice.

Video compression, while enabling efficient storage and transmission, inevitably impacts perceptual quality. The goal is to minimize this impact while maximizing compression. We achieve this by exploiting the limitations of the human visual system (HVS). The HVS is less sensitive to certain types of information, like high-frequency details and subtle changes in color. Compression algorithms leverage this by discarding or representing these less perceptible details with fewer bits. This process, however, introduces some level of loss. Think of it like summarizing a novel; you lose some details, but retain the main plot and characters. The more you compress, the more information is lost, and the more noticeable the quality degradation becomes.

For example, reducing the spatial resolution (lowering the number of pixels) directly reduces the amount of data, but also makes the image appear less sharp. Similarly, reducing the temporal resolution (decreasing the frame rate) can make motion appear less smooth.

Motion vectors are crucial in video compression because they describe the movement of objects between consecutive frames. Instead of encoding each frame independently, video compression algorithms identify similar areas between frames. The algorithm finds the best match for a block of pixels in the current frame within a previously encoded frame. The displacement from the matched block in the reference frame to the block in the current frame is defined by the motion vector. This vector only needs to store the displacement rather than encoding the entire block of pixels again. This is the core principle of inter-frame prediction.

For instance, imagine a scene where a person is walking across the screen. Instead of encoding the person’s position in every frame, the algorithm identifies the person’s position in the previous frame, generates a motion vector showing the displacement, and only encodes the difference between the predicted and actual position in the current frame. This significantly reduces the amount of data required for representation.

Quantization parameters (QP) directly control the level of compression and thus the quality of the reconstructed video. QP essentially determines the granularity of the quantization step. A lower QP value means finer quantization (smaller steps), resulting in higher quality but larger file size. Conversely, a higher QP value represents coarser quantization (larger steps), leading to more significant data reduction and a smaller file size but lower quality. Think of it as adjusting the number of shades of gray in an image: a low QP is like having many shades, providing smoother transitions and finer details. High QP reduces the number of shades, leading to blocky artifacts and loss of detail.

In practical terms, a QP of 20 might be suitable for high-quality video streaming, while a QP of 35 might be used for lower-quality, mobile-friendly video, prioritizing reduced bandwidth.

Measuring the performance of a video compression algorithm involves considering both objective and subjective metrics. Objective metrics use mathematical formulas to quantify compression efficiency and quality. Common metrics include:

• Bit rate: The amount of data (bits) used per second to represent the video. Lower bit rates are more efficient.
• PSNR (Peak Signal-to-Noise Ratio): Measures the difference between the original and compressed video. Higher PSNR values generally indicate better quality but don’t always correlate perfectly with perceived quality.
• SSIM (Structural Similarity Index): Considers perceptual aspects like luminance, contrast, and structure, providing a more accurate representation of perceived quality.

Subjective metrics involve human evaluation. Viewers rate the quality based on their perception of visual artifacts and overall quality. This is crucial because objective metrics may not always capture all aspects of perceived quality.

In a professional setting, a comprehensive analysis would consider both objective and subjective assessments, comparing the algorithm’s performance against established standards and existing codecs.

Compressed video often exhibits artifacts, which are visual impairments introduced by the compression process. Common artifacts include:

• Blocking artifacts: Appear as square blocks, especially noticeable in flat areas of the image, due to aggressive quantization.
• Ringing artifacts: Occur as halos or oscillations around sharp edges.
• Mosquito noise: Fine-grained noise patterns, often visible in areas of high detail.
• Blurring: Loss of fine details and sharpness.
• Motion artifacts: Jagged edges or shimmering in moving objects due to inaccuracies in motion compensation.

The severity of these artifacts depends on the compression algorithm used, the level of compression, and the characteristics of the video content.

Adaptive quantization adjusts the quantization parameter (QP) dynamically throughout the video frame or sequence. Instead of using a single, uniform QP, adaptive quantization assigns different QP values to different parts of the video based on their characteristics. Areas with high detail or complexity might receive a lower QP (finer quantization), while relatively uniform or less important areas might receive a higher QP (coarser quantization).

This approach improves the overall perceptual quality for a given bit rate because it allocates more bits to important visual information while compressing less important areas more aggressively. For example, a scene with a person’s face might have a lower QP to preserve detail, while the background might have a higher QP, reducing the overall bitrate without sacrificing face quality.

Future trends in video compression are driven by the ever-increasing demand for higher resolutions (8K, 16K), higher frame rates (120fps and beyond), and High Dynamic Range (HDR) content. This requires significant advancements in compression efficiency.

• AI-based compression: Leveraging machine learning and deep learning for more efficient and perceptually optimized compression schemes. This will move beyond traditional block-based methods and learn the complexities of the human visual system to improve visual quality while compressing better.
• Enhanced perceptual models: Refinements in HVS models will enable more precise prediction of the visibility of artifacts and smarter allocation of bits.
• Improved inter-prediction techniques: Development of advanced motion estimation and compensation techniques to more accurately predict and represent motion. This involves using more sophisticated motion models, potentially using neural networks.
• 3D video compression: Further development of efficient compression techniques for three-dimensional video and holographic content.
• Codec standardization: Continued evolution of established codecs like HEVC and the development of next-generation codecs such as VVC (Versatile Video Coding) and beyond.

These advancements will enable higher quality video experiences at lower bandwidth requirements, revolutionizing video streaming and content delivery.