Disclosure of Application No.
`08/411,369 (Ex. 1016)
`“Text and Image Sharpening of JPEG
`Compressed Images in the Frequency
`Domain.” Ex. 1016, p. 4, lines 5–6.
`“Figure 1 shows a block diagram of a
`typical implementation of the JPEG
`compression standard. The block
`diagram will be referred to as a
`compression engine. The compression
`engine 10 operates on source image
`data, which represents a source image in
`a given color space such as CIELAB.
`The source image data has a certain
`resolution, which is determined by how
`the image was captured. Each
`individual datum of the source image
`data represents an image pixel. The
`pixel further has a depth which is
`determined by the number of bits used
`to represent the image pixel.
`
`The source image data is typically
`formatted as a raster stream of data.
`The compression technique, however,
`requires the data to be represented in
`blocks. These blocks represent a two-
`dimensional portion of the source image
`data. The JPEG standard uses 8x8
`blocks of data. Therefore, a raster-to-
`block translation unit 12 translates the
`raster source image data into 8x8 blocks
`of source image data. The source image
`data is also shifted from unsigned
`integers to signed integers to put them
`into the proper format for the next stage
`in the compression process. These 8x8
`blocks are then forwarded to a discrete
`cosine transformer 16 via bus 14.
`
`Disclosure of US Patent No. 5,850,484
`(Ex. 1007)
`“Text and Image Sharpening of JPEG
`Compressed Images in the Frequency
`Domain.” Ex. 1007, at Title.
`“FIG. 1 shows a block diagram of a
`typical implementation of the JPEG
`compression standard. The block
`diagram will be referred to as a
`compression engine. The compression
`engine 10 operates on source image
`data, which represents a source image in
`a given color space such as CIELAB.
`The source image data has a certain
`resolution, which is determined by how
`the image was captured. Each
`individual datum of the source image
`data represents an image pixel. The
`pixel further has a depth which is
`determined by the number of bits used
`to represent the image pixel.
`
`The source image data is typically
`formatted as a raster stream of data.
`The compression technique, however,
`requires the data to be represented in
`blocks. These blocks represent a two-
`dimensional portion of the source image
`data. The JPEG standard uses 8x8
`blocks of data. Therefore, a raster-to-
`block translation unit 12 translates the
`raster source image data into 8x8 blocks
`of source image data. The source image
`data is also shifted from unsigned
`integers to signed integers to put them
`into the proper format for the next stage
`in the compression process. These 8x8
`blocks are then forwarded to a discrete
`cosine transformer 16 via bus 14.
`
`HUAWEI EX. 1017 - 1/6
`
`

`

`Disclosure of Application No.
`08/411,369 (Ex. 1016)
`
`The discrete cosine transformer 16
`converts the source image data into
`transformed image data using the
`discrete cosine transform (DCT). The
`DCT, as is known in the art of image
`processing, decomposes the 8x8 block
`of source image data into 64 DCT
`elements or coefficients, each of which
`corresponds to a respective DCT basis
`vector. These basis vectors are unique
`2-dimensional (2D) ‘spatial
`waveforms,’ which are the fundamental
`units in the DCT space. These basis
`vectors can be intuitively thought to
`represent unique images, wherein any
`source image can be decomposed into a
`weighted sum of these unique images.
`The discrete cosine transformer uses the
`forward discrete cosine (FDCT)
`function as shown below, hence the
`name.
`
`
`Disclosure of US Patent No. 5,850,484
`(Ex. 1007)
`
`The discrete cosine transformer 16
`converts the source image data into
`transformed image data using the
`discrete cosine transform (DCT). The
`DCT, as is known in the art of image
`processing, decomposes the 8x8 block
`of source image data into 64 DCT
`elements or coefficients, each of which
`corresponds to a respective DCT basis
`vector. These basis vectors are unique
`2-dimensional (2D) ‘spatial
`waveforms,’ which are the fundamental
`units in the DCT space. These basis
`vectors can be intuitively thought to
`represent unique images, wherein any
`source image can be 5 decomposed into
`a weighted sum of these unique images.
`The discrete cosine transformer uses the
`forward discrete cosine (FDCT)
`function as shown below, hence the
`name.
`
`
`
`
`
`
`The output transformer 16 is an 8x8
`block of DCT elements or coefficients,
`corresponding to the DCT basis vectors.
`This block of transformed image data is
`then forwarded to a quantizer 20 over a
`bus 18. The quantizer 20 quantizes the
`64 DCT elements using a 64-element
`quantization table 24, which must be
`
`
`
`
`The output of the transformer 16 is an
`8x8 block of DCT elements or
`coefficients, corresponding to the DCT
`basis vectors. This block of transformed
`image data is then forwarded to a
`quantizer 20 over a bus 18. The
`quantizer 20 quantizes the 64 DCT
`elements using a 64-element
`quantization table 24, which must be
`
`HUAWEI EX. 1017 - 2/6
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`

`

`Disclosure of Application No.
`08/411,369 (Ex. 1016)
`specified as an input to the compression
`engine 10. Each element of the
`quantization table is an integer value
`from one to 255, which specifies the
`stepsize of the quantizer for the
`corresponding DCT coefficient. The
`purpose of quantization is to achieve the
`maximum amount of compression by
`representing DCT coefficients with no
`greater precision than is necessary to
`achieve the desired image quality.
`Quantization is a many-to-one mapping
`and, therefore, is fundamentally lossy.
`As mentioned above, quantization tables
`have been designed which limit the
`lossiness to imperceptible aspects of the
`image so that the reproduced image is
`not perceptually different from the
`source image.
`
`
`The quantizer 20 performs a simple
`division operation between each DCT
`coefficient and the corresponding
`quantization table element. The
`lossiness occurs because the quantizer
`20 disregards any fractional remainder.
`Thus, the quanitzation function can be
`represented as shown in Equation 2
`below.
`
`Disclosure of US Patent No. 5,850,484
`(Ex. 1007)
`specified as an input to the compression
`engine 10. Each element of the
`quantization table is an integer value
`from one to 255, which specifies the
`stepsize of the quantizer for the
`corresponding DCT coefficient. The
`purpose of quantization is to achieve the
`maximum amount of compression by
`representing DCT coefficients with no
`greater precision than is necessary to
`achieve the desired image quality.
`Quantization is a many-to-one mapping
`and, therefore, is fundamentally lossy.
`As mentioned above, quantization tables
`have been designed which limit the
`lossiness to imperceptible aspects of the
`image so that the reproduced image is
`not perceptually different from the
`source image.
`
`
`The quantizer 20 performs a simple
`division operation between each DCT
`coefficient and the corresponding
`quantization table element. The
`lossiness occurs because the quantizer
`20 disregards any fractional remainder.
`Thus, the quantization function can be
`represented as shown in Equation 2
`below.
`
`
`where Y(k,l) represents the (k,l)-th DCT
`element and Q(k,l) represents the
`corresponding quantization table
`element.
`
`
`
`where Y(k,l) represents the (k,l)-th DCT
`element and Q(k,l) represents the
`corresponding quantization table
`element.
`
`
`HUAWEI EX. 1017 - 3/6
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`

`

`Disclosure of Application No.
`08/411,369 (Ex. 1016)
`To reconstruct the source image, this
`step is reversed, with the quantization
`table element being multiplied by the
`corresponding quantized DCT
`coefficient. The inverse quantization
`step can be represented by the following
`expression:
`
`
`Disclosure of US Patent No. 5,850,484
`(Ex. 1007)
`To reconstruct the source image, this
`step is reversed, with the quantization
`table element being multiplied by the
`corresponding quantized DCT
`coefficient. The inverse quantization
`step can be represented by the following
`expression:
`
`
`
`
`
`
`As should be apparent, the fractional
`part discarded during the quantization
`step is not restored. Thus, this
`information is lost forever. Because of
`the potential impact on the image
`quality of the quantization step,
`considerable effort has gone into
`designing the quantization tables. These
`efforts are described further below
`following a discussion of the final step
`in the JPEG compression technique.”
`Ex. 1016, at p. 4 , line 32 – p. 7, line 15.
`“These limitations significantly degrade
`text in color images because sharp edges
`are very important for reading
`efficiency.” Ex. 1016, p. 10, lines 28-
`29.
`“Accordingly, the need remains for a
`computationally efficient method for
`improving the visual quality of images,
`and in particular text, in scanned
`images.” Ex. 1016, p. 11, lines 16-18.
`
`
`
`
`As should be apparent, the fractional
`part discarded during the quantization
`step is not restored. Thus, this
`information is lost forever. Because of
`the potential impact on the image
`quality of the quantization step,
`considerable effort has gone into
`designing the quantization tables. These
`efforts are described further below
`following a discussion of the final step
`in the JPEG compression technique.”
`Ex. 1007, at 1:40-2:60.
`“These limitations significantly degrade
`text in color images because sharp edges
`are very important for reading
`efficiency.” Ex. 1007, at 4:44-46.
`
`“Accordingly, the need remains for a
`computationally efficient method for
`improving the visual quality of images,
`and in particular text, in scanned
`images.” Ex. 1007, at 4:65-67.
`
`HUAWEI EX. 1017 - 4/6
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`

`

`Disclosure of Application No.
`08/411,369 (Ex. 1016)
`“For edge sharpening in the frequency
`domain, the full image is first
`transformed into the frequency domain
`using the Fast Fourier Transform (FFT)
`or the Discrete Fourier Transform
`(DFT), low frequency components are
`dropped, and then the image is
`transformed back into the time domain.”
`Ex. 1016, p. 11, lines 9–14.
`“In general, compression and
`decompression are performed in
`conformance with the JPEG standard.”
`Ex. 1016, p. 11, lines 24–25.
`“By using the scaling matrix S, the
`high-frequency components of the DCT
`elements can be ‘enhanced’ without any
`additional computational requirements.”
`Ex. 1016, p. 12, lines 9–11.
`“The scanned image, although it can be
`any image, in the preferred embodiment
`is a printed version of the reference
`image. Thus, the variance of the scanned
`image represents the energy or
`frequency composition of the reference
`image but which is compromised by the
`inherent limitations of the scanner. The
`scaling matrix, therefore, boosts the
`frequency components that are
`compromised by the scanning process.
`
` A
`
` preferred embodiment of the
`invention is described herein in the
`context of a color facsimile (fax)
`machine. The color fax machine
`includes a scanner for rendering a color
`image into color source image data that
`represents the color image, a
`
` preferred embodiment of the
`invention is described herein in the
`context of a color facsimile (fax)
`machine. The color fax machine
`includes a scanner for rendering a color
`image into color source image data that
`represents the color image, a
`
`Disclosure of US Patent No. 5,850,484
`(Ex. 1007)
`“For edge sharpening in the frequency
`domain, the full image is first
`transformed into the frequency domain
`using the Fast Fourier Transform (FFT)
`or the Discrete Fourier Transform
`(DFT), low frequency components are
`dropped, and then the image is
`transformed back into the time domain.”
`Ex. 1007, at 4:56–61.
`“In general, compression and
`decompression are performed in
`conformance with the JPEG standard.”
`Ex. 1007, 5:5–7.
`“By using the scaling matrix S, the
`high-frequency components of the DCT
`elements can be ‘enhanced’ without any
`additional computational requirements.”
`Ex. 1007, 5:20–22.
`“The scanned image, although it can be
`any image, in the preferred embodiment
`is a printed version of the reference
`image. Thus, the variance of the scanned
`image represents the energy or
`frequency composition of the reference
`image but which is compromised by the
`inherent limitations of the scanner. The
`scaling matrix, therefore, boosts the
`frequency components that are
`compromised by the scanning process.
`
` A
`
`HUAWEI EX. 1017 - 5/6
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`

`

`Disclosure of Application No.
`08/411,369 (Ex. 1016)
`compression engine that compresses the
`color source image data to compressed
`image data, a means for encapsulating
`the compressed image data, and a means
`for transmitting the encapsulated data.
`The compression engine includes means
`for storing two quantization tables. The
`first quantization table is used to
`quantize the image data transformed
`using the discrete cosine transform
`(DCT). The second quantization table is
`encapsulated with the encoded
`quantized image data for use in
`decompressing the image. The second
`quantization table is related to the first
`quantization table in the manner
`described above. When used to transmit
`and receive color images between two
`locations, the machine transfers the
`images with higher quality than prior
`systems.” Ex. 1016, p. 9, line 24 - p. 10,
`line 15.
`“Although the compression engine
`according to the invention is
`implemented in dedicated hardware as
`described hereinabove, alternatively it
`can be implemented in software
`operating on a programmed computer
`having a microprocessor such as an Intel
`80486 or Pentium or Hewlett Packard
`PA-RISC.” Ex. 1016, p. 21, lines 18–
`22.
`
`
`Disclosure of US Patent No. 5,850,484
`(Ex. 1007)
`compression engine that compresses the
`color source image data to compressed
`image data, a means for encapsulating
`the compressed image data, and a means
`for transmitting the encapsulated data.
`The compression engine includes means
`for storing two quantization tables. The
`first quantization table is used to
`quantize the image data transformed
`using the discrete cosine transform
`(DCT). The second quantization table is
`encapsulated with the encoded
`quantized image data for use in
`decompressing the image. The second
`quantization table is related to the first
`quantization table in the manner
`described above. When used to transmit
`and receive color images between two
`locations, the machine transfers the
`images with higher quality than prior
`systems.” Ex. 1007, at 5:39-63.
`
`“Although the compression engine
`according to the invention is
`implemented in dedicated hardware as
`described hereinabove, alternatively it
`can be implemented in software
`operating on a programmed computer
`having a microprocessor such as an Intel
`80486 or Pentium or Hewlett Packard
`PA-RISC.” Ex. 1007, at 10:1–6.
`
`HUAWEI EX. 1017 - 6/6
`
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