US20020191699A1 - Detection system and method for enhancing digital video - Google Patents

Detection system and method for enhancing digital video Download PDF

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US20020191699A1
US20020191699A1 US10/171,360 US17136002A US2002191699A1 US 20020191699 A1 US20020191699 A1 US 20020191699A1 US 17136002 A US17136002 A US 17136002A US 2002191699 A1 US2002191699 A1 US 2002191699A1
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block
values
blocks
pixel
target block
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Royal O'Brien
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Digital Interactive Streams Inc
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/40—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using video transcoding, i.e. partial or full decoding of a coded input stream followed by re-encoding of the decoded output stream
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/527—Global motion vector estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression
    • H04N19/86—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression involving reduction of coding artifacts, e.g. of blockiness
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/01—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level
    • H04N7/0117—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level involving conversion of the spatial resolution of the incoming video signal
    • H04N7/012—Conversion between an interlaced and a progressive signal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/01—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level
    • H04N7/0117—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level involving conversion of the spatial resolution of the incoming video signal
    • H04N7/0122—Conversion of standards, e.g. involving analogue television standards or digital television standards processed at pixel level involving conversion of the spatial resolution of the incoming video signal the input and the output signals having different aspect ratios

Definitions

  • the present invention relates generally to processing digital video data, and in particular to a system and method for real-time enhancement of decoded digital video data to reduce artifacts and improve clarity of resolution and display.
  • a broadcast video may have 640 lines of horizontal resolution, and 480 vertical lines of resolution for a total of 307,200 pixels.
  • the colors of each pixel may be represented as a 3 character (byte) number (24 bit) having a Red, Green, and Blue (or YUV) value ranging from 0-255, for 16,777,216 (256 3 ) possible colors.
  • Converting the characters to bits, with 8 bits to each character (byte), drives the number up to 176,947,200 bits per second for film and 220,962,816 for NTSC video.
  • a 100 Megabit Ethernet network would not be able to transmit the data at the required rate. Given these realities, video data is typically compressed before transmission.
  • the Motion Picture Experts Group has embraced various well-known video encoding standards or processes, including MPEG-1, MPEG-2, and MPEG-4, which utilize similarities between successive image frames, referred to as temporal or inter-frame correlation, to provide inter-frame compression in which pixel-based representations of image frames are converted to motion representations. Similarities within image frames, referred to as spatial or intra-frame correlation, are also used to provide intra-frame compression in which the motion representations within an image frame are further compressed.
  • the basic approach divides the image into blocks and then individually transforms each block. Compression is realized by quantizing transform coefficients.
  • Such encoding techniques are calculation-intensive and include a high probability for error introduction, particularly during quantization steps of the encoding process.
  • block-based MPEG encoding leads to noticeable blocking artifacts, i.e., artificial discontinuities that appear between the boundaries of blocks.
  • blocking artifacts i.e., artificial discontinuities that appear between the boundaries of blocks.
  • a decoder reverses the encoding process, but the encoded errors remain.
  • artifacts are commonly referred to as artifacts, which are responsible for undesirable video image effects such as “mosaic” effect, distortion, mosquito noise, ringing noise and the like.
  • Some errors may derive from original source video.
  • the video may include undesired shifts in color or brightness for the same pixels representative of the same image between successive frames. Such shifts may be captured in the encoding process and propagated in the decoded data. This type of error is generally referred to as a standing block error herein.
  • a method for determining if a target block of decoded video data comprised of pixel values is a source of an artifact is disclosed.
  • the method is comprised of the steps of comparing values of determined pixels from the target block with pixels values from one or more reference blocks, said reference blocks being likely sources of artifacts, and assessing whether the compared values reveal substantial identity between the target block and a reference block.
  • FIG. 1 conceptually depicts an exemplary computer system for enhancing decoded video data in accordance with a preferred implementation of the present invention
  • FIG. 2 conceptually depicts basis functions (i.e., fundamental blocks), in the discrete cosine transformation grid, which are detected in accordance with a preferred implementation of the present invention
  • FIG. 3 depicts a table of basis functions in the discrete cosine transformation grid with a diagonal axis to conceptually illustrate its symmetrical properties, which are utilized in accordance with a preferred implementation of the present invention, and naming conventions used herein for certain types of blocks;
  • FIG. 4 conceptually depicts pixels in an 8 ⁇ 8 block, with the highlighted pixels used in a detection methodology in accordance with a preferred implementation of the present invention
  • FIGS. 5 through 8 are flowcharts conceptually illustrating an exemplary detection methodology in accordance with a preferred implementation of the present invention.
  • FIG. 9 is a high level flow chart of an exemplary methodology in accordance with a preferred implementation of the present invention.
  • FIG. 10 conceptually depicts two adjacent side-by-side 8 ⁇ 8 blocks of pixels for purposes of illustrating a block-specific correction for adjacent detected blocks in accordance with an exemplary implementation of the present invention
  • FIG. 11 conceptually depicts exemplary steps of a block-specific correction for a row of pixels in adjacent side-by-side detected blocks in accordance with a preferred implementation of the present invention
  • FIG. 12 conceptually depicts two diagonal 8 ⁇ 8 blocks for purposes of illustrating a block-specific correction implemented for diagonal detected blocks in accordance with an exemplary implementation of the present invention
  • FIG. 13 conceptually depicts four blocks including two diagonal detected fundamental blocks for purposes of illustrating a block-specific corrections implemented for the diagonal detected blocks in accordance with an exemplary implementation of the present invention.
  • FIGS. 14 through 16 conceptually depict a sequence of block-specific corrections implemented for diagonal detected blocks in accordance with an exemplary implementation of the present invention.
  • an exemplary computer system for enhancing decoded video data in accordance with the present invention preferably includes a bus 150 for communicating information, a central processing unit (CPU) 110 , a read only memory (ROM) 120 , random access memory (RAM) 130 and a frame buffer 140 . Additionally, a mass storage device 160 , a display device 170 and an input device 180 may be provided.
  • the storage device may include a hard disk, CD-ROM drive, DVD drive, tape drive, memory and/or other mass storage equipment.
  • the input device may include a communications link and/or other means for inputting data.
  • the computer system may include fewer, different and/or additional elements, provided it is capable, when programmed, of processing decoded (or decompressed) video data in accordance with the present invention.
  • the system may either stand alone or operate in a distributed environment.
  • the present invention may be implemented using a system comprised of a digital signal processor (DSP), an application-specific integrated circuit (ASIC), discrete gate logic, or other hardware, firmware, or any conventional programmable software module and a microprocessor.
  • DSP digital signal processor
  • ASIC application-specific integrated circuit
  • the software module could reside in RAM memory, flash memory, registers, or any other form of readable and writable storage medium known in the art.
  • An image on the display device 170 is made up of individual dots known as pixels (short for picture elements).
  • the frame buffer 140 which can be a designated part of main memory or a separate memory designed for image display, stores data representative of each pixel.
  • the display 170 is usually serially addressed, with pixels being sent to the display one at a time, starting at the top left corner of the screen and scanning along each line sequentially until the bottom right corner is reached.
  • frame buffer data is typically translated from binary to one or more voltages for each pixel.
  • many conventional architectures include two frame buffers.
  • the present invention provides a method of enhancing decoded digital video data while the data is still in a frame buffer without appreciably delaying display rendering.
  • the amount of time that is tolerable to complete enhancement is preferably no longer than the time between frames.
  • the time required can be no longer than approximately ⁇ fraction (1/30) ⁇ th of a second. If processing takes longer, it will begin to appreciably delay display rendering.
  • the subject invention is not limited to a specific source of compatible video data, a particular mode of operation (i.e., real-time or not real-time) or a particular delivery method. It may be utilized with streaming or stored video data. It may be applied in real-time or in a buffered or archival mode. Furthermore, it may be applied to process streaming video data received via a communications link (e.g., a DSL or cable modem connection), video data that has been downloaded and stored, and video data derived from a local source (e.g., video stored on a DVD).
  • a communications link e.g., a DSL or cable modem connection
  • video data that has been downloaded and stored e.g., video stored on a DVD.
  • While the system and methodology of the present invention are preferably applied to video data that has been encoded and decoded in accordance with MPEG standards (e.g., MPEG-2 and MPEG-4), those skilled in the art will appreciate that the present invention is directly applicable to other video encoding-decoding processes that employ the discrete cosine transform (DCT), including possible successors to MPEG-4 and methodologies for video conferencing and video telephony, such as those according to ITU-T standards. Such applications come within the scope of the present invention.
  • DCT discrete cosine transform
  • a detection methodology in accordance with the present invention may be applied to video data derived from transform-based video encoding-decoding processes (other than MPEG and ITU-T) that employ detectable patterns or functions (e.g., basis functions) for encoding blocks that are likely to be sources of error.
  • Correction methodologies in accordance with the present invention may be applied without the detection methodology of the present invention; although such an implementation may be less computationally efficient. Such applications also come within the scope of the present invention.
  • a system and method in accordance with the present invention may be applied to enhance encoded still images, such as JPEG images. In such case, each single image frame is addressed.
  • the images may be obtained from any number of sources, such as Internet web sites.
  • a system in accordance with the present invention may be integrated with a web browser to enhance the quality of uploaded images without appreciably delaying display rendering.
  • MPEG encoding removes redundant and less important information from video data prior to transmission.
  • the redundancies may include spatial and temporal redundancies (e.g., correlation between neighboring pixels, both within the same frame and between adjacent frames), entropy redundancy (e.g., frequent reoccurrence of some values more than others for any non-random digitized signal) and psycho-visual redundancy (e.g., details such as chromatic resolutions, spatial resolutions and temporal resolutions that are generally insignificant to human perception or entirely imperceptible).
  • MPEG decoding then reconstructs an approximation of the original image from the encoded data.
  • the methodology of a preferred implementation of the present invention utilizes decoded data stored in the frame buffer, and attempts to remove errors introduced or propagated during encoding to faithfully yield enhanced video.
  • the decoded data can be conceptually arranged in arrays of pixel. Images may be broken down into macroblocks, which are 16 ⁇ 16 sections of video on the screen. Macroblocks are broken down further to 8 ⁇ 8 sections known as submacroblocks (referred to herein as blocks), each of which represent one or more frequencies, i.e., basis functions, in the discrete cosine transformation grid such as that graphically depicted in FIG. 2.
  • the two dimensional DCT table of basis functions is highly symmetrical along a diagonal axis starting at the top left corner and running to the bottom right corner, as conceptually depicted in FIG. 3.
  • the symmetry is leveraged to facilitate detection in accordance with an exemplary detection methodology, such as the methodology described more fully below.
  • the top left block is a solid block.
  • the remaining blocks in the top row are referred to herein as ramps or horizontal frequency blocks.
  • the remaining blocks in the first column are referred to herein as ramps or vertical frequency blocks.
  • the remaining blocks in the other rows and columns are referred to herein as texture blocks.
  • the present invention recognizes that perceptibility of quantization error greatly varies with the DCT frequency, with singular frequency blocks, i.e., basis functions, being primary suspects for producing artifacts. These blocks typically represent either areas that the encoder identified as less important and allocated the least bandwidth to try to reproduce the video, or simplistic areas of video that do not require more than one frequency representation to produce the video. Thus, during encoding, blocks with less detail may have been accorded a lower priority (e.g., as background items) and therefore transformed only once (or twice) depending on the level of detail to be presented. These blocks, which are identical to or substantially similar to basis function blocks, are prime suspects for errors. In sharp contrast, blocks with more detail may have been given a higher priority (e.g., as foreground objects) and therefore transformed multiple times, consuming greater bandwidth.
  • a lower priority e.g., as background items
  • the methodology of a preferred implementation of the present invention analyzes decoded data in the frame buffer to identify singular frequency DCT blocks, as in step 910 .
  • the preferred analysis is efficient, taking into account symmetrical properties of the basis functions and using a relatively small number of computations to determine whether a block qualifies as a singular frequency (i.e., fundamental) DCT block. If a block so qualifies, then it is a target for detection and a candidate for block-specific error correction. By focusing such error correction processes on the detected fundamental blocks, the present invention efficiently allocates computing resources for real-time error correction.
  • These DCT basis function blocks are referred to as singular frequency, coarse frequency, coarse and fundamental blocks.
  • Color spaces are three-dimensional (3D) coordinate systems in which each color is represented by a single point. Colors appear as their primary components red, green and blue, in the RGB color space. Each color appears as a luminance (or luma) (brightness) component, Y, and two chrominance (or chroma) components, U and V, in the YUV space.
  • luminance or luma
  • chrominance or chroma
  • V 0.701 ⁇ R ⁇ 0.587 ⁇ G ⁇ 0.114 ⁇ B
  • YUV format is the defacto standard used for image and video compression.
  • UVYV the pixel format is four 8-bit values, which are stored as YVYU or UYVY. The four values correspond to luminance and chrominance for 2 pixels, with the lower priority chrominance values being shared between both pixels. To calculate the color and brightness for individual pixels adjacent UV values are combined.
  • RGB color space values
  • Blocks are encoded without regard to the neighboring block's color, so two hues or brightness values can be created with a simple DCT value and result in a “blocking” effect. Since the data produces a square display, the eye easily discerns different colored adjacent blocks.
  • An objective of the detection methodology of the present invention is to efficiently determine whether a block qualifies as a block that is a likely source of error (e.g., a fundamental block).
  • a preferred methodology employs a determined number of pixel locations within each 8 ⁇ 8 block and rules derived from the symmetrical properties of the DCT basis functions.
  • the detection methodology preferably detects fundamental blocks, or gives up, within a defined maximum number of steps (e.g., 10) per block. A large number of steps in a detection methodology may render it unsuitable for real-time application.
  • binary luminance values are used for the detection methodology.
  • chrominance values may be used in addition to (or in lieu) of the luminance values.
  • a preferred detection methodology entails a process of elimination using determined pixel locations within each 8 ⁇ 8 block, such as the 15 possible pixel locations shown as shaded regions in FIG. 4. Comparisons of chosen pixel values according to determined rules reveal if a block is a fundamental block. Those skilled in the art will appreciate that other pixel locations and a different number of pixel locations may be used in the detection methodology, provided that application of the corresponding rules enables detection of fundamental blocks. Such other pixel locations and numbers of pixels come within the scope of the present invention.
  • An exemplary detection methodology is conceptually illustrated in the flowcharts provided as FIGS. 5 through 8, and as step 910 in the high-level flowchart provided as FIG. 9.
  • Other detection processes which logically determine whether blocks correspond to fundamental DCT blocks, will also work, and come within the scope of the present invention. For instance, alternative processes of elimination can readily be devised in accordance with the disclosed detection process.
  • the detection methodology begins with steps for checking for inverted luminance. Absent an inversion, ( 0 , 0 ) should always be greater or equal to its immediately adjacent pixels horizontally or vertically.
  • the ( 0 , 0 ) value is compared with the ( 1 , 0 ) value. If ( 0 , 0 ) is not greater than ( 1 , 0 ), then ( 0 , 0 ) is compared with ( 0 , 1 ), as in step 506 . If ( 0 , 0 ) ⁇ ( 0 , 1 ) then the block is deemed to have an inverted luminance value.
  • step 504 If either of the logical checks in steps 504 and 506 is true, then the block is deemed to be normal and logic moves to the Hi check, steps 512 - 516 . In the event of inversion, the detection methodology proceeds accordingly, but all greater than signs>and less than signs ⁇ are switched as in step 500 . Otherwise, the inversion would lead to spurious results.
  • the methodology determines if the block is a vertical or a horizontal fundamental block (VFB and HFB herein, respectively). First ( 0 , 0 ) is compared with ( 0 , 7 ), as in step 514 . If these values are equal then the block is a possible HFB. If not, it is a VFB, or a diagonally combined block (DCB herein) and control passes to step 602 in FIG. 6, and detection processing proceeds.
  • VFB and HFB horizontal fundamental block
  • ( 0 , 0 ) is compared with ( 0 , 3 ), as in step 514 . If these values are equal then the block is either an (x, 0 ) or an (x, 4 ) block.
  • These possibilities include ( 0 , 0 ), ( 1 , 0 ), ( 2 , 0 ), ( 3 , 0 ), ( 4 , 0 ), ( 5 , 0 ), ( 6 , 0 ), ( 7 , 0 ), ( 0 , 4 ), ( 1 , 4 ), ( 2 , 4 ), ( 3 , 4 ), ( 4 , 4 ), ( 5 , 4 ), ( 6 , 4 ), ( 7 , 4 )—16 of 64 possible blocks, eliminating 50% of the possible blocks from the previous step.
  • the block is either an (x, 2 ) or (x, 6 ) block.
  • These possibilities include ( 0 , 2 ), ( 1 , 2 ), ( 2 , 2 ), ( 3 , 2 ), ( 4 , 2 ), ( 5 , 2 ), ( 6 , 2 ), ( 7 , 2 ), ( 0 , 6 ), ( 1 , 6 ), ( 2 , 6 ), ( 3 , 6 ), ( 4 , 6 ), ( 5 , 6 ), ( 6 , 6 ), ( 7 , 6 )—16 of 64 possible blocks.
  • each comparison eliminates a percentage of the remaining possibilities until only one possibility is left.
  • the foregoing detection methodology is applied to each block in each video frame, identifying those blocks that are fundamental DCT blocks.
  • information pertaining to such blocks is stored for efficient reference purposes, such as in a table, step 915 in FIG. 9.
  • the information includes a high value for the block, a low value for the block, the frequency and DCT coefficient, as well as one or more additional items, such as a flag and auxiliary bit. All blocks that are not fundamental blocks are identified using a default identifier.
  • the table thus reveals each fundamental block, and provides sufficient information to reveal the block's position and to reconstruct the block. This information is used to determine whether a block-specific correction is warranted, and, if so, then the type of correction that is warranted.
  • block-specific correction methodologies are preferably applied, as in step 920 .
  • the block-specific corrections are intended to reduce certain propagated errors and errors introduced by quantization during the encoding process. Error correction methodologies known in the art may be utilized without departing from the scope of the present invention. In addition to such methodologies known in the art, or instead of such methodologies, error correction processes described herein may be employed. These error correction processes include standing block corrections, adjacent block corrections and diagonal block corrections. The objective is to reduce the errors to which the detected blocks are particularly conducive.
  • An important aspect of a preferred implementation of the present invention is computing efficiency. Binary arithmetic is used, large processor-specific register sets are utilized, and floating point arithmetic is avoided.
  • the preferred methodology of the present invention leverages such computing techniques to enhance performance, facilitate real-time implementation and reduce cost.
  • a preferred implementation of the present invention favors manipulation of registers over memory transfers from the system and even on-board cache. This reduces time consuming reads in and out of the CPU.
  • a preferred implementation of the present invention also favors binary divisions by two.
  • values are first converted from 8-bit values to 16-bit for precision. The division is accomplished with a shift right by 1 bit.
  • each value is first converted from 8 to 16 bit through unpacking or bit manipulation. Then the values are added. The division is accomplished by a shift right 1 bit in the result. Then the result is packed back up or otherwise manipulated to 8-bit.
  • the average of 50 ( 00110010 ) and 100 ( 01100100 ) is preferably accomplished by converting the values to 16-bit (0000000000110010 and 0000000001100100, respectively), adding them to get the sum 0000000010010110, and shifting the sum by 1 bit to the right to yield 0000000001001011, and then restoring the result to 8-bit ( 01001011 ).
  • the average of 50 ( 00110010 ) and 100 ( 01100100 ) is 75 ( 01001011 ).
  • a preferred implementation of the present invention leverages large register sets, such as the Intel® MMX register set which includes eight 64-bit registers. By using larger registers, more pixels can be processed per instruction cycle. This leads to more efficient use of available computing resources and, consequently, lower cost.
  • Adjacent fundamental blocks having a difference in luminosity and/or color e.g., a difference between averages for blocks
  • the range is based upon perception and computational efficiency.
  • block-specific corrections apply only once to an edge (i.e., an edge adjacent to an edge of a fundamental block). If a block edge has been previously modified then the correction methodology skips that edge and goes to the next edge, to prevent additional calculations which can result in distortion.
  • the adjacent block correction method entails blending or smoothing to create a gradual transition from one DCT frequency to the other. Weighted averages are computed for pixels in a determined number of columns on each side of the shared edge for both blocks. In a preferred implementation, the number of columns is 4, though other amounts (e.g., 3 columns) may be utilized without departing from the scope of the present invention.
  • FIG. 10 adjacent left (L) and right (R) blocks are shown.
  • the blocks share an edge along L( 7 , 0 ) to L( 7 , 7 ) and R( 0 , 0 ) to R( 0 , 7 ).
  • FIG. 11 provides a graphical representation of steps of the correction methodology for the first row.
  • the values in columns L 7 and R 0 are replaced with averages, as in step 1110 .
  • L( 7 , 0 ) and R( 0 , 0 ) are replaced with their average 75, A 1 .
  • the average A 1 equals the sum of L( 7 , 0 ) and R( 0 , 0 ) divided by two, with the division being performed by a bit shift right as discussed above.
  • the third pixel away from the edge is replaced with its average with A 1 .
  • L( 5 , 0 ) is replaced with (L( 5 , 0 )+A 1 )/2, as in step 1120 .
  • the average of L( 5 , 0 ), i.e., 50 (00110010 in binary), and A 1 , i.e., 75 (01001011 in binary) equals 62.5
  • the result of the computation (A 3 L) is actually 62 (00111110 in binary) because all amounts less than one are truncated.
  • R( 2 , 0 ) is replaced with (R( 2 , 0 )+A 1 )/2.
  • the result is 010101 1 1 (i.e., 87), referred to herein as A 3 R.
  • the second pixel away from the edge is replaced with the average of the averages computed above.
  • L( 6 , 0 ) is replaced with (A 1 +A 3 L)/2, i.e. 01000100 (68), A 2 L, as in step 1130 .
  • R( 1 , 0 ) is replaced with (A 1 +A 3 R)/2, i.e., 01010001 (81), A 2 R.
  • the fourth pixel away from the edge is replaced with the average of the itself and the value in the pixel adjacent pixel which is closest to the shared edge.
  • L( 3 , 0 ) is replaced with (L( 4 , 0 )+A 3 L)/2, i.e. 00111000 (56), A 4 L, as in step 1140 .
  • R( 3 , 0 ) is replaced with (R( 3 , 0 )+A 3 R)/2, i.e., 01011101 (93), A 4 R.
  • a block may adjacent to more than one fundamental DCT block.
  • a fundamental block may be positioned vertically above another fundamental block, which may be positioned horizontally adjacent to yet another fundamental block. In each case, processing proceeds along the shared edges, except that pixels that have been previously modified are not modified again.
  • the adjacent block correction methodology described above also applies to correcting solid to solid, ramp or texture blocks.
  • the right block could be a ramp or texture block.
  • L( 7 , 0 ) and R( 0 , 0 ) may be replaced with their average 75. Then the remaining three pixels L( 6 , 0 ) to L( 4 , 0 ) in the left block and the remaining three pixels R( 1 , 0 ) to R( 3 , 0 ) in the right block can be adjusted to provide a gradual transition from 75 to 50 and from 75 to 100 in a number of ways, including linearly.
  • the decrement and increment may be determined by two successive divisions by 2 of the transitional difference (25).
  • 25 (00011001) divided by 2 equals 12 (00001100); and 12 (00001100) divided by 2 equals 6 (00000011).
  • L( 6 , 0 ), L( 5 , 0 ) and (L 4 , 0 ) may be adjusted linearly by equal decrements of 6 to 69, 63 and 57, respectively.
  • R( 1 , 0 ), R( 2 , 0 ) and R( 3 , 0 ) may be adjusted linearly by equal increments of 6 to 81, 87 and 93, respectively.
  • a diagonal block correction entails blending or smoothing to approximate a diagonal line.
  • DCT-based transforms commonly employ diagonal coarse frequency blocks.
  • the top right block TR and the bottom left block BL, in FIG. 12 may be solid blocks.
  • the diagonal block correction methodology provides a weighted adjustment of neighboring pixel regions (each a “correction region”), preferably such as the generally triangular shaded regions in blocks TL and BR, as conceptually shown in FIG. 13.
  • the diagonal blocks e.g., TR and BL
  • the diagonal blocks should be analyzed to determine whether they fall within an acceptable range.
  • Fundamental blocks having a difference in luminosity and/or color that falls within a determined range are corrected according to the diagonal block correction methodology.
  • the range is based upon perception and computational efficiency.
  • the correction region is preferably defined by the edges of blocks BR and TL (each a “correction block”) that are shared with the diagonal fundamental blocks BL and TR (each a “shared edge”) and a diagonal line passing through the non-intersecting corners of the shared edges for each block being corrected (i.e., BR and TR).
  • the shared edges include 1310 and 1350 for TL, and 1330 and 1340 for BR.
  • the diagonal lines run from 0,7 to 7,0 for BR, and from 0,7 to 7,0 for TL. Pixels on the diagonal line are preferably included in the region; although they may be excluded without substantially compromising the correction.
  • a preferred starting pixel for correction processing is a pixel in the correction region that is immediately adjacent to the intersection of the shared edges for the correction block.
  • the starting pixel for BR in FIG. 13 is 0,0, and for TL is 7,7.
  • the correction process moves from the starting pixel along a row or column parallel to either shared edge.
  • each pixel in the correction region is addressed (and possibly adjusted) up to the last pixel that is in the row or column being addressed, within the correction region and adjacent to the opposite end of the shared edge.
  • correction processing resumes with the next parallel row or column in the correction region, starting at the pixel that is adjacent to the starting pixel for the previous row or column. Correction processing continues until each pixel in the correction region has been addressed (and possibly adjusted).
  • correction processing may proceed from 0,0 to 7,0; 0,1 to 6,1; 0,2 to 5,2; 0,3 to 4,3; 0,4 to 3,4; 0,5 to 2,5; 0,6 to 1,6; and 0,7.
  • correction processing for BR may proceed as follows: from 0,0 to 0,7; 1,0 to 1,6; 2,0 to 2,5; 3,0 to 3,4; 4,0 to 4,3; 5,0 to 5,2; 6,0 to 6,1; and 7,0. Either way is acceptable.
  • correction processing may proceed from 7,7 to 7,0; 6,7 to 6,1; 5,7 to 5,2; 4,7 to 4,3; 3,7 to 3,4; 2,7 to 2,5; 1,7 to 1,6; and 0,7.
  • correction processing for TL may proceed as follows: from 7,7 to 0,7; 7,6 to 1,6; 7,5 to 2,5; 7,4 to 3,4; 7,3 to 4,3; 7,2 to 5,2; 7,1 to 6,1; and 7,0.
  • Correction processing for each pixel in a correction region preferably entails adjusting to provide a smooth transition between blocks BL and TR and a more precisely defined diagonal. While various averaging, combing or blending techniques may be employed, in a preferred implementation each pixel in the correction region is replaced with the average of the current pixel values for the immediately adjacent pixels closest to the shared edges. Thus, in FIG. 13, each pixel in the correction region will be replaced with the average of the current pixel values for the pixels immediately above and to the left of the pixel being addressed. Thus, 0,0 is replaced with the average of 0,7 and 7,0. Next, 1,0 is replaced with the average of 1,7 and the current value of 0,0.
  • step 1410 through 1580 exemplary correction processing is conceptually illustrated for the first two rows of the correction region for block BR.
  • the boxed value is replaced with the average of the two circled values.
  • the value of 0,0 is replaced with the average of 40 and 50, i.e., 45.
  • the replacement value becomes the current value for that pixel and is reflected in all subsequent steps, i.e., steps 1410 through 1580 .
  • All averaging is preferably accomplished using a binary arithmetic process, such as the shift right process discussed above. In such case, all fractions are lost due to truncation.
  • the average of 41 and 40 is expressed as 40.
  • step 1420 pixel 1,0 is replaced with the average of the then current value for pixel 0,0 (i.e., 45) and 40. This process continues for each pixel in the correction region.
  • FIG. 16 conceptually displays block BR upon completion of such processing.
  • the correction methodology achieves a gradual transition between blocks BL and TR. It also adds greater definition to the diagonal intended through those blocks.
  • standing block correction Another correction methodology applied in an exemplary implementation of a preferred embodiment of the present invention is known as standing block correction.
  • the flow of photons used to generate video images of physical subjects naturally fluctuates. While many such fluctuations are imperceptible to the human visual system, they may be captured by conventional video and photography equipment, leading to variations in luminance and chrominance values over time. When encoded and accentuated in an 8 ⁇ 8 block, such variations are often perceptible.
  • Standing block correction removes or smoothes such fluctuations.
  • Solid DCT fundamental blocks are identified as such in accordance with the detection methodology described above. Information pertaining to such blocks is stored for efficient reference purposes, such as in a table, step 915 in FIG. 9. Preferably, the information includes current luminance and chrominance values, as well as a flag for the first occurrence of the values in that block.
  • a flagged solid DCT fundamental block is known as a first time standing anchor.
  • an exemplary standing block correction methodology Upon detecting a DCT fundamental solid block at the same location in the next frame, an exemplary standing block correction methodology first determines whether the difference between the first time standing anchor and the current block falls within a determined range.
  • the range preferably represents a perceptible change in luminance and/or chrominance values, but not so stark as to be an intended variation.
  • the range may be, for example, from 4 to 32 for luminance and/or chrominance values. If the difference falls within the range, the current block is flagged as a standing block, and a standing block correction is applied to it.
  • the correction may entail replacing the current values with values for the first time standing anchor. Alternatively, the standing block and first time standing anchor values may be blended, such as by averaging.
  • the standing block values may be replaced with averages.
  • Each successive standing block is then compared with each previous standing block.
  • Standing block corrections are applied, until the differences fall outside the determined range. After the values are changed in a standing block, it is processed as a normal solid block in relation to the other blocks surrounding it.
  • Various error correction or filtering techniques may be applied globally to the detected fundamental and remaining blocks to enhance the displayed image, as step 930 in FIG. 9.
  • the global corrections do not alter pixels that have already been modified according to a block-specific correction.
  • Color values (in YUV color space) of a block are compared against corresponding block color value arrays in subsequent video images. Should change in values or frequency exceed a threshold value or range of values, comparison is conducted against adjacent blocks arrays. Such comparison verifies if similar degrees of change are performed in adjacent blocks that would represent motion, rather than error. Should the change in values or frequency be unique as compared to adjacent block array representations, the difference in color values of the target block array as compared against corresponding previous block color values is calculated. Based on the comparison results, the minimum and maximum color values, as well as the DCT frequency, are adjusted accordingly to correct the block.
  • an “edge” is identified for filter processing.
  • an “edge” may include either a bottom horizontal row of 8 pixels on the border of a block or a right vertical column of 8 pixels on the border of a block. While an “edge” is depicted as either a bottom horizontal row or right vertical column, it is generally applicable to any linearly aligned consecutive pixels within a block.
  • Fields a and b preferably denote horizontal rows or vertical columns of adjacent block edges. Ideally, each nth pixel in an “edge” is denoted in either field a or b, and individually includes YUV values to create a plurality of colors.
  • a copy of the video image frame is held for reference. Once a copy of the image is created, a threshold value is established. Ideally, the threshold value is determined as a measure of human perception of various shades of color. In general, a human being is capable of perceiving 13 gradients of shading in a single YUV color hue.
  • a section of a block of pixels is identified for filtering.
  • pixels in an “edge” are selected for filtering.
  • every YUV value per selected pixel is processed by the filtering method. Should the YUV value for the processed pixels all be identical, the filtering process may initiate noise correction. However, should the YUV values for the processed pixels vary amongst selected pixels, the filtering system may enable edge correction.
  • the pixels in the “edge” are preferably evaluated against corresponding pixels in the following video image frame to conduct temporal correction.
  • a process for filtering noise distortions includes applying statistical computations to the selected pixels to perform immediate noise correction. Such statistical computation is performed recursively until every pixel in an “edge” has been corrected. Preferable recursive statistical computations to calculate corrected YUV vector values for a given pixel ‘n’ in a selected “edge” field a or “edge” field b include:
  • An exemplary recursive statistical application for calculating an adjusted pixel to correct noise distortion includes calculating corrected individual YUV values for a given pixel in field a. Such calculation preferably entails taking the sum of individual YUV values for the nth pixel in field a plus double a corresponding nth pixel's YUV values in field b. Weighted corrected measures of individual YUV values are preferably determined by multiplying the calculated sum values for individual YUV by one-third (1 ⁇ 3). Such computation captures pixel YUV values in field b to be contributed to the correction of the given nth pixel in field a.
  • Further correction of noise distortion may be enabled by calculating corrected individual YUV values for a given corresponding pixel in field b.
  • Such calculation preferably entails taking the sum of individual YUV values for an nth pixel in field b plus double a corresponding nth pixel's YUV values in field a.
  • Such calculation preferably references an original image frame, as copied, to ensure accurate noise correction.
  • the weighted corrected measures of individual YUV values for a pixel in field b are preferably determined by multiplying the calculated sum values for individual YUV by one-third (1 ⁇ 3).
  • Such computation captures YUV values for the nth pixel in field a to be contributed to the correction of the given nth pixel in field b.
  • the resulting weighted corrected measures of individual YUV values for corresponding pixels in fields a and b are encoded into the video image frame for display. Once corrected, pixel values are entered into the display. The next step is to ascertain whether another pixel is available within the “edge” for processing, as illustrated. Should there be a pixel available on the “edge,” the process recommences for the available pixel. Alternatively, should the correction for the “edge” be complete and no pixels remain for processing, the filtering methodology halts recursive statistical computations until another “edge” is selected for processing.
  • Another process for filtering “edge” corrections includes applying statistical computations to selected pixels to identify any “edge” corrections to be performed. Such statistical computation is performed recursively until every pixel in an “edge” has been assessed and where necessary, corrected. Preferable recursive statistical computations to calculate corrected YUV vector values for a given pixel ‘n’ in a selected “edge” field a corresponding to “edge” field b include:
  • the generated statistical computation (1) for YUV vector values in the nth pixel is preferably applied to an established threshold value. Should absolute difference amounts in individual YUV values fall within the established threshold values based on human perception, no “edge” correction field is generated, as illustrated, and further pixels in fields a and b are selected for processing. Alternatively, should individual YUV difference values fall outside established threshold YUV values, corrected YUV amounts for given pixels in fields a and b are preferably generated.
  • Exemplary statistical application steps for calculating an adjusted pixel to correct “edge” distortion include calculating corrected individual YUV values for a given pixel in field a. Such calculation preferably entails taking the sum of individual YUV values for a pixel in field a plus double a corresponding pixel's YUV values in field b. Weighted corrected measures of individual YUV values are preferably determined by multiplying the calculated sum values for individual YUV by one-third (1 ⁇ 3). Such computation captures pixel YUV values in field b to be contributed to the correction of given pixel in field a.
  • Further correction of noise distortion may be enabled by calculating corrected individual YUV values for a given corresponding pixel in field b.
  • Such calculation preferably entails taking the sum of individual YUV values for a pixel in field b plus double a corresponding pixel's YUV values in field a.
  • Such calculation preferably references an original image frame, as copied, to ensure accurate “edge” correction.
  • the weighted corrected measures of individual YUV values for a pixel in field b are preferably determined by multiplying the calculated sum values for individual YUV by one-third (1 ⁇ 3).
  • Such computation captures pixel YUV values in field a to be contributed to the correction of given pixel in field b.
  • the resulting weighted corrected measures of individual YUV values for corresponding pixels in fields a and b are encoded into the video image frame for display.
  • the next step is to ascertain whether another pixel is available within the “edge” for processing. Should there be a pixel available on the “edge,” the process recommences for such pixel.
  • the filtering methodology halts recursive statistical computations until another “edge” is selected for processing.
  • a “temporal” correction step to ensure consistency in resolution between video representation frames may also be applied.
  • “Edge” pixels, processed either in the previous noise correction filter or edge correction filter, are categorized into frames i and i+1 to denote contiguous frames in an interlaced sequence of video frames.
  • Each nth pixel in a selected “edge” is in frame i or i+1; wherein each nth pixel has delineated YUV values to create a plurality of colors.
  • YUV values respectively represent red, green, and blue phosphor colors having varying intensities.
  • Preferable recursive statistical computation to calculate corrected YUV vector values for a given nth pixel in a selected “edge” field in frame i or i+1 include:
  • Generated statistical computation (1) for YUV values in the nth pixel between frames i and i+1 is preferably applied to established threshold R, G, and B values. Should absolute difference amounts in individual YUV values fall within the established threshold values based on human perception, no “edge” correction field is generated, as illustrated, and further pixels in frames i and i+1 are selected for processing. Alternatively, should individual YUV difference values fall outside established threshold YUV values, corrected YUV amounts for given pixels in frame i+1 are preferably generated.
  • An exemplary statistical application step for calculating an adjusted pixel to correct temporal distortion includes calculating corrected individual R,G, B values for a given pixel in frame i+1. Such calculation preferably entails taking the sum of individual YUV values for a pixel in frame i+1 plus double a corresponding pixel's YUV values in frame i. Weighted corrected measures of individual YUV values are preferably determined by multiplying the calculated sum values for individual YUV by one-third (1 ⁇ 3). Such computation captures pixel R, G, and B values in frame i to be contributed to the correction of given pixel in frame i+1.
  • the resulting weighted corrected measures of individual YUV values for pixels in frames i+1 are encoded into the video image frame i+1 for display.
  • the next step is to ascertain whether another pixel is available within the “edge” for processing. Should there be a pixel available on the “edge,” the process recommences for such pixel. Alternatively, should the correction for the “edge” be complete and no pixels remain for processing, the filtering methodology halts recursive statistical computations until another “edge” is selected for processing.
  • the methodology of the present invention is not dependent upon a particular codec, it can be applied to correct video encoded according to many different standards such as MPEG-1, MPEG-2 and any other DCT-based compression.
  • Application to other compression methods may require changing block scan sizes, locations, and thresholds being applied to the decoded video data, all of which may be readily determined by those skilled in the art.

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WO2002102050A2 (fr) 2002-12-19

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