It is known that a prefilter, which is often used in a preprocess of video encoding, is effective for reducing block distortion, mosquito noise, or the like, accompanied with encoding, thereby improving the subjective image quality. The pass bandwidth (called “bandwidth” below) of the used prefilter is limited, so as to reduce noise included in an original image and improve the encoding efficiency. However, if the bandwidth is narrowed too much, the image quality is extremely degraded.
FIG. 23 shows an image processing method including a band limitation.
As shown in FIG. 23, in the image processing method including the band limitation, first, original image data B(1) is input, and is then converted into a frequency component 41) (see step S1000). The frequency component I(1) is subjected to a band limitation using a bandwidth r1 (0<r1<1), so that a frequency component I(r1) is obtained (see step S1100). The frequency component I(r1) is subjected to image transformation, thereby generating filtered image data B(r1) (see step S1200).
When such image processing is applied to all frames of a video image by using the same bandwidth, image quality of each filtered frame is not equal because each frame has the individual frequency characteristics of the image. That is, an image having a large amount of low-frequency components has only a small difference from the original image, and thus degradation in the subjective and objective image qualities is small. However, in an image having a large amount of high-frequency components, edges or the like are smoothed and blurred, which extremely degrades subjective and objective image qualities.
As an objective image estimation value, for example, a PSNR (Peak Signal to Noise Ratio) is often used. With given signal level (S) and noise level (N), the PSNR is indicated by the following formula:PSNR=20×log10(S/N)
In actual processing, if brightness of an original image is represented by 8 bit (i.e., 0 to 255), the PSNR can be computed by the following formula:
      P    ⁢                  ⁢    S    ⁢                  ⁢    N    ⁢                  ⁢    R    =      20    ⁢                  ⁢                  log        10            [                        255          /                      1            N                          ⁢                                            ∑                              x                =                0                                            N                -                1                                      ⁢                                          ∑                                  y                  =                  0                                                  N                  -                  1                                            ⁢                                                {                                                            f                      ⁡                                              (                                                  x                          ,                          y                                                )                                                              -                                                                  f                        ′                                            ⁡                                              (                                                  x                          ,                          y                                                )                                                                              }                                2                                                        ]      where N indicates the number of pixels of the original image and a filtered image thereof; f(x,y) indicates each pixel value of the original image; and f(x,y) indicates each pixel value of the filtered image. Additionally, “255” indicates the maximum amplitude (or pixel value) of the pixels of both images.
That is, in actual processing, the original image and the filtered image thereof are compared with each other (specifically by using the above formula), so as to compute the PSNR.
In a method for solving the above-described problem, subjective and objective image quality control is performed by means of a “round-robin” band limitation applied to each image.
FIG. 24 shows the structure of an optimum filtered image generating apparatus 100 for generating optimum filtered image data by performing a “round-robin” band limitation.
As shown in FIG. 24, the optimum filtered image generating apparatus 1000 includes an original image data input unit 1100, a frequency component analyzing unit 1200, a bandwidth manual selecting unit 1300, a band limitation unit 1400, an image data generating unit 1500, a PSNR computing unit 1600, an image judgment unit 1700, and an optimum band-limited image data output unit 1800.
FIG. 25 shows an image processing method of generating optimum filtered image data by performing a “round-robin” band limitation, where the method is executed in the optimum filtered image generating apparatus 1000 having the above structure.
In the optimum filtered image generating apparatus 1000, first, original image data B(1) is input into the original image data input unit 1100, and is then converted into a frequency component 41) in the frequency component analyzing unit 1200 (see step S2000).
Next, in the bandwidth manual selecting unit 1300, a provisional bandwidth r1 is manually selected (see step S2100). Then, in the band limitation unit 1400, the converted frequency component I(1) is subjected to a band limitation using the selected bandwidth r1, so as to obtain a frequency component I(r1) (see step S2200).
Next, in the image data generating unit 1500, the frequency component I(r1) is subjected to an image transformation, thereby generating image data B(r1) (see step S2300). In the PSNR computing unit 1600, the original image data B(1) is compared with the image data B(r1), so as to compute RSNR (r1) (indicated by “P(r1)” below) (see step S2400).
In the image judgment unit 1700, it is determined whether or not the computed P(r1) has a desired image quality (see step S2500). If it has the desired image quality, the optimum band-limited image data output unit 1800 outputs the image data B(r1) as optimum band-limited image data (i.e., optimum filtered image data) (see step S2600).
However, it is rare that P(r1) obtained in the first processing turn has a desired image quality. When it does not have the desired image quality, the operation returns to the process (in step S2100) performed by the bandwidth manual selecting unit 1300, and a bandwidth (r2) is selected again so that the relevant band-limited image has a quality closer to the desired image quality. Then, band limitation, image generation, and PSNR computation are again performed similarly.
That is, the above-described operation is repeated N times until the desired image quality is obtained, and a bandwidth rN, which is obtained finally, is used as an optimum bandwidth for generating image data B(rN) by the optimum band-limited image data output unit 1800. The generated image data B(rN) is output as optimum band-limited image data (i.e., optimum filtered image data) (see step S2600).
However, in the above method, various video images and all frames which form thereof are subjected to filtering, the subjective or objective image quality of each obtained image signal is estimated, and the relevant operation is repeated in a “round-robin” manner until an equal image quality is obtained for all frames of the video images. In consideration of the required time and cost, when many images are processed, the above method is inappropriate and impracticable.
In order to solve the above problem, in a known technique (see Patent Document 1), image processing is performed by obtaining an optimum bandwidth based on the encoding data of a (video) image.
FIG. 26 shows the structure of an optimum filtered image generating apparatus 2000 for generating optimum filtered image data by using encoding data.
As shown in FIG. 26, the optimum filtered image generating apparatus 2000 includes an original image data input unit 2100, a frequency component analyzing unit 2200, an image data encoding unit 2300, an optimum limited bandwidth determination unit 2400, a band limitation unit 2500, an image data generating unit 2600, and an optimum band-limited image data output unit 2700.
FIG. 27 shows an image processing method of generating optimum filtered image data by using encoding data, where the method is executed in the optimum filtered image generating apparatus 2000 having the above structure.
In the optimum filtered image generating apparatus 2000, first, original image data B(1) is input into the original image data input unit 2100, and is then converted into a frequency component 41) in the frequency component analyzing unit 2200 (see step S3000).
Next, in the image data encoding unit 2300, the input original image data B(1) is encoded (see step S3100). Based on the information for the amount of code obtained by the relevant encoding, an optimum bandwidth r1 is determined in the optimum limited bandwidth determination unit 2400 (see step S3200).
In the band limitation unit 2500, the converted frequency component 41) is subjected to a band limitation using the determined bandwidth r1, so as to obtain a frequency component I(r1) (see step S3300). In the image data generating unit 2600, the frequency component I(r1) is subjected to an image transformation, thereby generating image data B(r1) (see step S3400).
Finally, the image data B(r1) is output as optimum band-limited image data (i.e., optimum filtered image data) from the optimum band-limited image data output unit 2700 (see step S3500).
Accordingly, in the conventional optimum filtered image generating apparatus 2000 formed as shown in FIG. 26, after encoding is performed, an optimum bandwidth is determined based on encoding data obtained by the encoding. Therefore, optimum filtered image data is obtained without performing a repetitive operation as required in the optimum filtered image generating apparatus 1000 fowled as shown in FIG. 24.    Patent Document 1: Japanese Unexamined Patent Application, First Publication No. H06-225276.