Digital Video: An Introduction to MPEG-2 (Digital Multimedia Standards Series)

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No other frames are predicted from a B-frame.

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Because of this, a very low bitrate B-frame can be inserted, where needed, to help control the bitrate. If this was done with a P-frame, future P-frames would be predicted from it and would lower the quality of the entire sequence. However, similarly, the future P-frame must still encode all the changes between it and the previous I- or P- anchor frame.

B-frames can also be beneficial in videos where the background behind an object is being revealed over several frames, or in fading transitions, such as scene changes. A B-frame can contain any number of intra-coded blocks and forward-predicted blocks, in addition to backwards-predicted, or bidirectionally predicted blocks. MPEG-1 has a unique frame type not found in later video standards. D-frames or DC-pictures are independent images intra-frames that have been encoded using DC transform coefficients only AC coefficients are removed when encoding D-frames—see DCT below and hence are very low quality.

D-frames are never referenced by I-, P- or B- frames. D-frames are only used for fast previews of video, for instance when seeking through a video at high speed.

Digital Video: An Introduction to MPEG-2

Given moderately higher-performance decoding equipment, fast preview can be accomplished by decoding I-frames instead of D-frames. This provides higher quality previews, since I-frames contain AC coefficients as well as DC coefficients.

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If the encoder can assume that rapid I-frame decoding capability is available in decoders, it can save bits by not sending D-frames thus improving compression of the video content. For this reason, D-frames are seldom actually used in MPEG-1 video encoding, and the D-frame feature has not been included in any later video coding standards. MPEG-1 operates on video in a series of 8x8 blocks for quantization. However, because chroma color is subsampled by a factor of 4, each pair of red and blue chroma blocks corresponds to 4 different luma blocks.

This set of 6 blocks, with a resolution of 16x16, is called a macroblock. A macroblock is the smallest independent unit of color video. Motion vectors see below operate solely at the macroblock level. If the height or width of the video are not exact multiples of 16, full rows and full columns of macroblocks must still be encoded and decoded to fill out the picture though the extra decoded pixels are not displayed.

To decrease the amount of temporal redundancy in a video, only blocks that change are updated, up to the maximum GOP size. This is known as conditional replenishment. However, this is not very effective by itself. Through motion estimation the encoder can compensate for this movement and remove a large amount of redundant information. The encoder compares the current frame with adjacent parts of the video from the anchor frame previous I- or P- frame in a diamond pattern, up to a encoder-specific predefined radius limit from the area of the current macroblock.

If a match is found, only the direction and distance i. The reverse of this process, performed by the decoder to reconstruct the picture, is called motion compensation. A predicted macroblock rarely matches the current picture perfectly, however.

The larger the error, the more data must be additionally encoded in the frame. For efficient video compression, it is very important that the encoder is capable of effectively and precisely performing motion estimation. Motion vectors record the distance between two areas on screen based on the number of pixels called pels. The finer the precision of the MVs, the more accurate the match is likely to be, and the more efficient the compression. There are trade-offs to higher precision, however.

Finer MVs result in larger data size, as larger numbers must be stored in the frame for every single MV, increased coding complexity as increasing levels of interpolation on the macroblock are required for both the encoder and decoder, and diminishing returns minimal gains with higher precision MVs.

Half-pel was chosen as the ideal trade-off. Because neighboring macroblocks are likely to have very similar motion vectors, this redundant information can be compressed quite effectively by being stored DPCM -encoded.


Digital Video: An Introduction to MPEG-2 (Digital Multimedia Standards Series) [ Barry G. Haskell, Atul Puri, Arun N. Netravali] on *FREE* shipping. Digital Video offers comprehensive coverage of the MPEG-2 audio/visual digital compression standard. The treatment includes the specifics needed to.

Only the smaller amount of difference between the MVs for each macroblock needs to be stored in the final bitstream. P-frames have one motion vector per macroblock, relative to the previous anchor frame. B-frames, however, can use two motion vectors; one from the previous anchor frame, and one from the future anchor frame. An even more serious problem exists with macroblocks that contain significant, random, edge noise , where the picture transitions to typically black.

All the above problems also apply to edge noise. In addition, the added randomness is simply impossible to compress significantly. All of these effects will lower the quality or increase the bitrate of the video substantially. Each 8x8 block is encoded by first applying a forward discrete cosine transform FDCT and then a quantization process. In reality, there are some sometimes large rounding errors introduced both by quantization in the encoder as described in the next section and by IDCT approximation error in the decoder.

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Just a moment while we sign you in to your Goodreads account. Compression formats Compression software codecs. To ask other readers questions about Digital Video , please sign up. No trivia or quizzes yet. This book is not yet featured on Listopia. The encoder compares the current frame with adjacent parts of the video from the anchor frame previous I- or P- frame in a diamond pattern, up to a encoder-specific predefined radius limit from the area of the current macroblock. It has lower complexity than Layer II to facilitate real-time encoding on the hardware available circa

Prior to , it was specified by IEEE The FDCT process converts the 8x8 block of uncompressed pixel values brightness or color difference values into an 8x8 indexed array of frequency coefficient values. One of these is the statistically high in variance DC coefficient , which represents the average value of the entire 8x8 block. The other 63 coefficients are the statistically smaller AC coefficients , which are positive or negative values each representing sinusoidal deviations from the flat block value represented by the DC coefficient.

Since the DC coefficient value is statistically correlated from one block to the next, it is compressed using DPCM encoding. Only the smaller amount of difference between each DC value and the value of the DC coefficient in the block to its left needs to be represented in the final bitstream.

Additionally, the frequency conversion performed by applying the DCT provides a statistical decorrelation function to efficiently concentrate the signal into fewer high-amplitude values prior to applying quantization see below. Quantization of digital data is, essentially, the process of reducing the accuracy of a signal, by dividing it into some larger step size i. The frame-level quantizer is either dynamically selected by the encoder to maintain a certain user-specified bitrate, or much less commonly directly specified by the user.

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Contrary to popular belief, a fixed frame-level quantizer set by the user does not deliver a constant level of quality. Instead, it is an arbitrary metric that will provide a somewhat varying level of quality, depending on the contents of each frame.

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Given two files of identical sizes, the one encoded at an average bitrate should look better than the one encoded with a fixed quantizer variable bitrate. Constant quantizer encoding can be used, however, to accurately determine the minimum and maximum bitrates possible for encoding a given video. A quantization matrix is a string of numbers which tells the encoder how relatively important or unimportant each piece of visual information is. Each number in the matrix corresponds to a certain frequency component of the video image.

Quantization is performed by taking each of the 64 frequency values of the DCT block, dividing them by the frame-level quantizer, then dividing them by their corresponding values in the quantization matrix. Finally, the result is rounded down. This significantly reduces, or completely eliminates, the information in some frequency components of the picture. Typically, high frequency information is less visually important, and so high frequencies are much more strongly quantized drastically reduced. MPEG-1 actually uses two separate quantization matrices, one for intra-blocks I-blocks and one for inter-block P- and B- blocks so quantization of different block types can be done independently, and so, more effectively.

This quantization process usually reduces a significant number of the AC coefficients to zero, known as sparse data which can then be more efficiently compressed by entropy coding lossless compression in the next step. Quantization eliminates a large amount of data, and is the main lossy processing step in MPEG-1 video encoding. This is also the primary source of most MPEG-1 video compression artifacts , like blockiness , color banding , noise , ringing , discoloration , et al. This happens when video is encoded with an insufficient bitrate, and the encoder is therefore forced to use high frame-level quantizers strong quantization through much of the video.

Several steps in the encoding of MPEG-1 video are lossless, meaning they will be reversed upon decoding, to produce exactly the same original values. Since these lossless data compression steps don't add noise into, or otherwise change the contents unlike quantization , it is sometimes referred to as noiseless coding. The coefficients of quantized DCT blocks tend to zero towards the bottom-right. Maximum compression can be achieved by a zig-zag scanning of the DCT block starting from the top left and using Run-length encoding techniques.

Digital Video: An introduction to MPEG-2 (Digital Multimedia by Barry G. Haskell PDF

Run-length encoding RLE is a very simple method of compressing repetition. A sequential string of characters, no matter how long, can be replaced with a few bytes, noting the value that repeats, and how many times. For example, if someone were to say "five nines", you would know they mean the number: RLE is particularly effective after quantization, as a significant number of the AC coefficients are now zero called sparse data , and can be represented with just a couple of bytes.

This is stored in a special 2- dimensional Huffman table that codes the run-length and the run-ending character. Huffman Coding is a very popular method of entropy coding, and used in MPEG-1 video to reduce the data size. The data is analyzed to find strings that repeat often. Those strings are then put into a special table, with the most frequently repeating data assigned the shortest code.

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