Similar RGB Color

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StringsGreedy Algorithms

You are given a string color representing the hexadecimal representation of an RGB color. The string starts with '#' followed by exactly six hexadecimal characters (0-9, a-f), representing the red, green, and blue components. Each component consists of two hexadecimal characters.

A similar RGB color is defined as a color where each pair of hexadecimal digits are the same. For example, "#112233" and "#aabbaa" are similar RGB colors.

Your task is to find the similar RGB color that is the closest to the given color. The "closeness" is defined as the Euclidean distance between the RGB values. You should return the similar RGB color as a string in the same hexadecimal format (with the leading '#').

For example:

Input: color = "#09f166" Output: "#111111" Explanation: The similar colors are "#000000", "#111111", "#222222", ..., "#ffffff".
- "#09f166" is closest to "#111111" because if you break it down:
  - The red part "09" is closest to "11"
  - The green part "f1" is closest to "ff"
  - The blue part "66" is closest to "66" The distance formula is not explicitly required, but think about each hex pair's integer representation's difference and how you'd optimize the hex digit to minimize that difference.
Input: color = "#4e3f4f" Output: "#554455"

Similar RGB Color

Easy

Google

1 view

Topics:

StringsGreedy Algorithms

You are given a string color representing the hexadecimal representation of an RGB color. The string starts with '#' followed by exactly six hexadecimal characters (0-9, a-f), representing the red, green, and blue components. Each component consists of two hexadecimal characters.

A similar RGB color is defined as a color where each pair of hexadecimal digits are the same. For example, "#112233" and "#aabbaa" are similar RGB colors.

Your task is to find the similar RGB color that is the closest to the given color. The "closeness" is defined as the Euclidean distance between the RGB values. You should return the similar RGB color as a string in the same hexadecimal format (with the leading '#').

For example:

Input: color = "#09f166" Output: "#111111" Explanation: The similar colors are "#000000", "#111111", "#222222", ..., "#ffffff".
- "#09f166" is closest to "#111111" because if you break it down:
  - The red part "09" is closest to "11"
  - The green part "f1" is closest to "ff"
  - The blue part "66" is closest to "66" The distance formula is not explicitly required, but think about each hex pair's integer representation's difference and how you'd optimize the hex digit to minimize that difference.
Input: color = "#4e3f4f" Output: "#554455"

Solution

Clarifying Questions

When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:

The input color is a string representing an RGB value (e.g., "#1A2B3C"). Can I assume it will always be a valid 6-character hexadecimal representation with a leading '#', and can each pair of characters after the '#' represent a hex value from 00 to FF?
What constitutes a 'similar' RGB color? Is it defined as minimizing the Euclidean distance in RGB space, and if so, is integer arithmetic sufficient for calculating this distance, or do I need to consider floating-point precision?
The problem asks for the 'similar' RGB color. If there are multiple RGB colors that result in the same minimum distance, is any one of them acceptable as the output, or is there a specific tie-breaking rule (e.g., lexicographical order)?
Each component of the RGB color (#RR, #GG, #BB) needs to be 'similar'. Am I constrained to only the shorthand hexadecimal colors like #AA, #BB, etc. or am I trying to find the closest in a full range of 256^3 possible RGB values?
If the input is already in the shorthand format (e.g., "#112233"), should I simply return the original input, or should I still search the space to see if a different shorthand hex value is closer to the initial RGB value?

Brute Force Solution

Approach

The brute force approach in this case means trying all possible simplified RGB colors and finding the one that is closest to the original color. We generate every possible simple RGB color and then calculate how different it is from the input RGB color.

Here's how the algorithm would work step-by-step:

Consider that each component of the RGB color can only take on a limited set of values (00, 11, 22, 33, 44, 55, 66, 77, 88, 99, AA, BB, CC, DD, EE, FF).
Generate every possible combination of these simple red, green, and blue values.
For each of these simplified RGB colors, calculate the difference between it and the original RGB color.
Compare all of these differences and choose the simplified RGB color that has the smallest difference from the original. This is our closest similar color.

Code Implementation

def similar_rgb_color(color):
    possible_values = ['00', '11', '22', '33', '44', '55', '66', '77', '88', '99', 'aa', 'bb', 'cc', 'dd', 'ee', 'ff']
    minimum_distance = float('inf')
    closest_color = ''

    # Iterate through possible combinations of R, G, and B
    for red_value in possible_values:
        for green_value in possible_values:
            for blue_value in possible_values:
                simplified_color = red_value + green_value + blue_value

                # Convert hex colors to integers for distance calculation
                red_component = int(color[0:2], 16)
                green_component = int(color[2:4], 16)
                blue_component = int(color[4:6], 16)
                simplified_red = int(simplified_color[0:2], 16)
                simplified_green = int(simplified_color[2:4], 16)
                simplified_blue = int(simplified_color[4:6], 16)

                # Calculate the Euclidean distance between the two colors
                distance = ((red_component - simplified_red) ** 2 +
                            (green_component - simplified_green) ** 2 +
                            (blue_component - simplified_blue) ** 2)

                # Find color with minimal color distance
                if distance < minimum_distance:
                    minimum_distance = distance
                    closest_color = simplified_color

    return closest_color

Big(O) Analysis

Time Complexity

O(1) – The algorithm iterates through a fixed set of simplified RGB color values. Since each color component (red, green, blue) can only take on 16 possible hexadecimal values (00, 11,... FF), the total number of possible simplified RGB colors is 16 * 16 * 16. This number is constant, independent of any input. Therefore, the time complexity is O(1).

Space Complexity

O(1) – The provided solution iterates through all possible combinations of simplified RGB values and calculates the difference with the original RGB color. The number of possible simplified RGB values is fixed (16 possible values for each component, so 16 * 16 * 16 total combinations). The algorithm only needs to store a few variables to hold the current simplified RGB color being evaluated, the minimum difference found so far, and the corresponding closest RGB color. Since the number of variables needed doesn't depend on the input RGB color, the auxiliary space used is constant.

Optimal Solution

Approach

The goal is to find the simplest RGB color that's closest to a given RGB color. The key idea is to convert each color component (Red, Green, Blue) to its closest simplified version, and then combine those simplified components.

Here's how the algorithm would work step-by-step:

Separate the given RGB color into its Red, Green, and Blue components.
For each component (Red, Green, Blue), find the nearest 'simple' value. Simple values are 00, 11, 22, 33, 44, 55, 66, 77, 88, 99, AA, BB, CC, DD, EE, FF in hexadecimal.
To find the closest simple value, think about the component value as a number between 0 and 255. For example, for the Red component, find which of the simple values is closest to it. You can do this by considering the first digit, and then checking whether rounding up or down to the nearest simple number gives the smaller difference.
Replace the original Red, Green, and Blue components with these closest simple values.
Combine the simplified Red, Green, and Blue values back together to form the final, simplified RGB color.

Code Implementation

def similar_rgb_color(color): 
    hex_digits = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f']

    def nearest_simple_value(component):
        component_value = int(component, 16)
        
        #Find the closest simple value by comparing with the first digit.
        first_digit = component[0]
        
        lower_value = first_digit + first_digit
        lower_value_int = int(lower_value, 16)
        
        upper_value_index = hex_digits.index(first_digit) + 1
        if upper_value_index > 15:
            upper_value = 'ff'
        else:
            upper_value = hex_digits[upper_value_index] + hex_digits[upper_value_index]
        upper_value_int = int(upper_value, 16)
        
        if abs(component_value - lower_value_int) <= abs(component_value - upper_value_int):
            return lower_value
        else:
            return upper_value

    red_component = color[1:3]
    green_component = color[3:5]
    blue_component = color[5:7]
    
    simplified_red = nearest_simple_value(red_component)
    simplified_green = nearest_simple_value(green_component)
    simplified_blue = nearest_simple_value(blue_component)
    
    # Combine the simplified components to produce the final color
    return '#' + simplified_red + simplified_green + simplified_blue

Big(O) Analysis

Time Complexity

O(1) – The algorithm processes a single RGB color, which consists of three components (Red, Green, Blue). For each component, it finds the nearest simple value from a fixed set of 16 possible values. This operation takes constant time. Since the number of components is fixed (3), the overall time complexity remains constant, irrespective of any input size. Therefore, the time complexity is O(1).

Space Complexity

O(1) – The algorithm operates on individual color components (Red, Green, Blue) and calculates the closest 'simple' value for each. It doesn't create any auxiliary data structures that scale with the input color string. Temporary variables are used for calculations, but their number is constant and independent of the input size. Therefore, the space complexity is constant.

Edge Cases

Case	How to Handle
Input string is null or empty	Return null or empty string to indicate invalid input, depending on requirements.
Input string length is not exactly 6 characters	Return an error or a specific value indicating invalid format since RGB requires 6 hex characters.
Input string contains non-hexadecimal characters	Return an error or filter out invalid characters since RGB is defined using hex values 0-9 and a-f.
All input RGB components are already 'rounded' (e.g., '00', '11', '22', 'EE')	The algorithm should still return the original input, as it is already in the desired format.
The closest rounding requires going below '00' or above 'FF' for any component	Clamp the rounded value to the valid range of '00' to 'FF' for proper hexadecimal representation.
Large number of calls to the function in a loop (performance implications)	Ensure the rounding function is computationally efficient to avoid performance bottlenecks by precomputing the possible rounded values.
The input string starts with '#'	Strip the '#' character before processing the string, or handle the case where '#' is present in the logic.
Integer overflow when converting hex to decimal or vice versa	Use appropriate data types to prevent integer overflow during calculations and ensure the values remain within the allowed RGB range.